html_url stringlengths 48 51 | title stringlengths 5 280 | comments stringlengths 63 51.8k | body stringlengths 0 36.2k ⌀ | comment_length int64 16 1.52k | text stringlengths 159 54.1k | embeddings listlengths 768 768 |
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https://github.com/huggingface/datasets/issues/4085 | datasets.set_progress_bar_enabled(False) not working in datasets v2 | One important thing for beginner like me is: from datasets.utils.logging import disable_progress_bar
Do not forget the 'utils' or you will waste a long time like me.... | ## Describe the bug
datasets.set_progress_bar_enabled(False) not working in datasets v2
## Steps to reproduce the bug
```python
datasets.set_progress_bar_enabled(False)
```
## Expected results
datasets not using any progress bar
## Actual results
AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled
## Environment info
datasets version 2
| 26 | datasets.set_progress_bar_enabled(False) not working in datasets v2
## Describe the bug
datasets.set_progress_bar_enabled(False) not working in datasets v2
## Steps to reproduce the bug
```python
datasets.set_progress_bar_enabled(False)
```
## Expected results
datasets not using any progress bar
## Actual results
AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled
## Environment info
datasets version 2
One important thing for beginner like me is: from datasets.utils.logging import disable_progress_bar
Do not forget the 'utils' or you will waste a long time like me.... | [
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https://github.com/huggingface/datasets/issues/4084 | Errors in `Train with Datasets` Tensorflow code section on Huggingface.co | Hi @blackhat-coder, thanks for reporting.
Please note that the `transformers` library updated their data collators API last year (version 4.10.0):
- huggingface/transformers#13105
now requiring to pass `return_tensors` argument at Data Collator instantiation.
And therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators.
If you would like to follow our examples, please update your installed `transformers` version:
```
pip install -U transformers
``` | ## Describe the bug
Hi
### Error 1
Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors'
### Error 2
`DataCollatorWithPadding` isn't imported
## Steps to reproduce the bug
```python
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer
dataset = load_dataset('glue', 'mrpc', split='train')
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
```
This is the same code on Huggingface.co
## Actual results
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
## Environment info
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.7
- PyArrow version: 6.0.0
- Pandas version: 1.4.1
> | 69 | Errors in `Train with Datasets` Tensorflow code section on Huggingface.co
## Describe the bug
Hi
### Error 1
Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors'
### Error 2
`DataCollatorWithPadding` isn't imported
## Steps to reproduce the bug
```python
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer
dataset = load_dataset('glue', 'mrpc', split='train')
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
```
This is the same code on Huggingface.co
## Actual results
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
## Environment info
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.7
- PyArrow version: 6.0.0
- Pandas version: 1.4.1
>
Hi @blackhat-coder, thanks for reporting.
Please note that the `transformers` library updated their data collators API last year (version 4.10.0):
- huggingface/transformers#13105
now requiring to pass `return_tensors` argument at Data Collator instantiation.
And therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators.
If you would like to follow our examples, please update your installed `transformers` version:
```
pip install -U transformers
``` | [
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https://github.com/huggingface/datasets/issues/4080 | NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset | Hi @richarddwang,
Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well):
- #4002
That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:
- installing `datasets` from our GitHub repo:
```bash
pip install git+https://github.com/huggingface/datasets#egg=datasets
```
- forcing the data files to be redownloaded
```python
ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload")
```
Feel free to re-open this issue if the problem persists.
Duplicate of:
- #4031 | ## Steps to reproduce the bug
```python
datasets.load_dataset("conll2012_ontonotesv5", "english_v12")
```
## Actual results
```
Downloading builder script: 32.2kB [00:00, 9.72MB/s]
Downloading metadata: 20.0kB [00:00, 10.4MB/s]
Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size
, total: 379.12 MiB) to ...
Traceback (most recent call last): [315/390]
File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module>
train()
File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train
trainer.fit(model, datamodule=dm)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte
rrupt
return trainer_fn(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run
self._data_connector.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre
pare_data
self.trainer.datamodule.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn
fn(*args, **kwargs)
File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data
raw_dsets = datasets.load_dataset(**load_dataset_kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 | 83 | NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset
## Steps to reproduce the bug
```python
datasets.load_dataset("conll2012_ontonotesv5", "english_v12")
```
## Actual results
```
Downloading builder script: 32.2kB [00:00, 9.72MB/s]
Downloading metadata: 20.0kB [00:00, 10.4MB/s]
Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size
, total: 379.12 MiB) to ...
Traceback (most recent call last): [315/390]
File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module>
train()
File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train
trainer.fit(model, datamodule=dm)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte
rrupt
return trainer_fn(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run
self._data_connector.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre
pare_data
self.trainer.datamodule.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn
fn(*args, **kwargs)
File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data
raw_dsets = datasets.load_dataset(**load_dataset_kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
Hi @richarddwang,
Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well):
- #4002
That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:
- installing `datasets` from our GitHub repo:
```bash
pip install git+https://github.com/huggingface/datasets#egg=datasets
```
- forcing the data files to be redownloaded
```python
ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload")
```
Feel free to re-open this issue if the problem persists.
Duplicate of:
- #4031 | [
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https://github.com/huggingface/datasets/issues/4075 | Add CCAgT dataset | Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you
PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable. | ## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible | 44 | Add CCAgT dataset
## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible
Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you
PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable. | [
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https://github.com/huggingface/datasets/issues/4075 | Add CCAgT dataset | HI, I was waiting to come out in the second version to do the implementation.
- Paper: https://dx.doi.org/10.2139/ssrn.4126881
- Data: [Data mendelay](http://doi.org/10.17632/wg4bpm33hj.2) | ## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible | 22 | Add CCAgT dataset
## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible
HI, I was waiting to come out in the second version to do the implementation.
- Paper: https://dx.doi.org/10.2139/ssrn.4126881
- Data: [Data mendelay](http://doi.org/10.17632/wg4bpm33hj.2) | [
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https://github.com/huggingface/datasets/issues/4074 | Error in google/xtreme_s dataset card | Hi @wranai, thanks for reporting.
Please note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752).
If that information is wrong, feel free to contact the paper's authors to suggest that correction.
Just note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia... | **Link:** https://huggingface.co/datasets/google/xtreme_s
Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).
| 74 | Error in google/xtreme_s dataset card
**Link:** https://huggingface.co/datasets/google/xtreme_s
Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).
Hi @wranai, thanks for reporting.
Please note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752).
If that information is wrong, feel free to contact the paper's authors to suggest that correction.
Just note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia... | [
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https://github.com/huggingface/datasets/issues/4071 | Loading issue for xuyeliu/notebookCDG dataset | Hi @Jun-jie-Huang,
As the error message says, ".pkl" data files are not supported.
If you would like to share your dataset on the Hub, you would need:
- either to create a Python loading script, that loads the data in any format
- or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...)
You can find the details in our docs:
- How to share a dataset: https://huggingface.co/docs/datasets/share
- How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script
Feel free to re-open this issue and ping us if you need further assistance. | ## Dataset viewer issue for '*xuyeliu/notebookCDG*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)*
*Couldn't load the xuyeliu/notebookCDG with provided scripts: *
```
from datasets import load_dataset
dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl")
```
I get an error message as follows:
FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
Am I the one who added this dataset ? No
| 103 | Loading issue for xuyeliu/notebookCDG dataset
## Dataset viewer issue for '*xuyeliu/notebookCDG*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)*
*Couldn't load the xuyeliu/notebookCDG with provided scripts: *
```
from datasets import load_dataset
dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl")
```
I get an error message as follows:
FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
Am I the one who added this dataset ? No
Hi @Jun-jie-Huang,
As the error message says, ".pkl" data files are not supported.
If you would like to share your dataset on the Hub, you would need:
- either to create a Python loading script, that loads the data in any format
- or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...)
You can find the details in our docs:
- How to share a dataset: https://huggingface.co/docs/datasets/share
- How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script
Feel free to re-open this issue and ping us if you need further assistance. | [
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https://github.com/huggingface/datasets/issues/4062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | Hi @aapot, thanks for reporting.
We are investigating the cause of this issue. We will keep you informed. | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| 18 | Loading mozilla-foundation/common_voice_7_0 dataset failed
## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
Hi @aapot, thanks for reporting.
We are investigating the cause of this issue. We will keep you informed. | [
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https://github.com/huggingface/datasets/issues/4062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | When making HTTP request from code line:
```
response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
```
it cannot be decoded to JSON because it raises a 404 Not Found error.
The request is fixed if removing the `/{use_cdn}` from the URL.
Maybe there was a change in the Common Voice API?
CC: @anton-l @patrickvonplaten @polinaeterna | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| 52 | Loading mozilla-foundation/common_voice_7_0 dataset failed
## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
When making HTTP request from code line:
```
response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
```
it cannot be decoded to JSON because it raises a 404 Not Found error.
The request is fixed if removing the `/{use_cdn}` from the URL.
Maybe there was a change in the Common Voice API?
CC: @anton-l @patrickvonplaten @polinaeterna | [
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] |
https://github.com/huggingface/datasets/issues/4062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | I have also made the hotfix for all the rest of Common Voice script versions: 8.0, 6.1, 6.0,..., 1.0 | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| 19 | Loading mozilla-foundation/common_voice_7_0 dataset failed
## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
I have also made the hotfix for all the rest of Common Voice script versions: 8.0, 6.1, 6.0,..., 1.0 | [
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https://github.com/huggingface/datasets/issues/4062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | Hi @ngoquanghuy99! The dataset should load fine if you go through the following steps:
1. Go to https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0 and click "Access repository" if you see a message about sharing your contact information with Mozilla Foundation at the top of the page. If you've already done that then skip to step 2.
2. Run the command `huggingface-cli login` in your terminal or notebook to authenticate your machine.
3. Load the dataset with `use_auth_token=True`:
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_9_0", "ab", use_auth_token=True)
``` | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| 83 | Loading mozilla-foundation/common_voice_7_0 dataset failed
## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
Hi @ngoquanghuy99! The dataset should load fine if you go through the following steps:
1. Go to https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0 and click "Access repository" if you see a message about sharing your contact information with Mozilla Foundation at the top of the page. If you've already done that then skip to step 2.
2. Run the command `huggingface-cli login` in your terminal or notebook to authenticate your machine.
3. Load the dataset with `use_auth_token=True`:
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_9_0", "ab", use_auth_token=True)
``` | [
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https://github.com/huggingface/datasets/issues/4062 | Loading mozilla-foundation/common_voice_7_0 dataset failed | > Thanks @anton-l I could load the dataset now, but in another way. Thanks anyways!
Can you share the "another way" please? | ## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
| 22 | Loading mozilla-foundation/common_voice_7_0 dataset failed
## Describe the bug
I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN")
```
## Expected results
load `mozilla-foundation/common_voice_7_0` dataset succesfully
## Actual results
```
JSONDecodeError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
909 try:
--> 910 return complexjson.loads(self.text, **kwargs)
911 except JSONDecodeError as e:
/opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)
524 and not use_decimal and not kw):
--> 525 return _default_decoder.decode(s)
526 if cls is None:
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3)
369 s = str(s, self.encoding)
--> 370 obj, end = self.raw_decode(s)
371 end = _w(s, end).end()
/opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3)
399 idx += 3
--> 400 return self.scan_once(s, idx=_w(s, idx).end())
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
JSONDecodeError Traceback (most recent call last)
/tmp/ipykernel_358/370980805.py in <module>
1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split
----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True)
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1690 ignore_verifications=ignore_verifications,
1691 try_from_hf_gcs=try_from_hf_gcs,
-> 1692 use_auth_token=use_auth_token,
1693 )
1694
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
670 split_dict = SplitDict(dataset_name=self.name)
671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
673
674 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager)
151
152 self._log_download(self.config.name, bundle_version, hf_auth_token)
--> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template))
154
155 if self.config.version < datasets.Version("5.0.0"):
~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template)
130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
--> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
133 return response["url"]
134
/opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs)
915 raise RequestsJSONDecodeError(e.message)
916 else:
--> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
918
919 @property
JSONDecodeError: [Errno Expecting value] Not Found: 0
```
## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
> Thanks @anton-l I could load the dataset now, but in another way. Thanks anyways!
Can you share the "another way" please? | [
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https://github.com/huggingface/datasets/issues/4061 | Loading cnn_dailymail dataset failed | Hi @Arij-Aladel, thanks for reporting.
This issue was already reported
- #3784
and its root cause is a change in the Google Drive service. See:
- #3786
We have already fixed it in our 2.0.0 release. See:
- #3787
Please, update your `datasets` version:
```
pip install -U datasets
```
and retry loading the dataset by forcing its redownload:
```python
dataset = load_dataset("cnn_dailymail", "3.0.0", download_mode="force_redownload")
``` | ## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
## Expected results
load `cnn_dailymail` dataset succesfully
## Actual results
failed to load and get error
> NotADirectoryError: [Errno 20] Not a directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` 1.8.0:
- Platform: Ubuntu-20.04
- Python version: 3.9.10
- PyArrow version: 3.0.0
| 66 | Loading cnn_dailymail dataset failed
## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
## Expected results
load `cnn_dailymail` dataset succesfully
## Actual results
failed to load and get error
> NotADirectoryError: [Errno 20] Not a directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` 1.8.0:
- Platform: Ubuntu-20.04
- Python version: 3.9.10
- PyArrow version: 3.0.0
Hi @Arij-Aladel, thanks for reporting.
This issue was already reported
- #3784
and its root cause is a change in the Google Drive service. See:
- #3786
We have already fixed it in our 2.0.0 release. See:
- #3787
Please, update your `datasets` version:
```
pip install -U datasets
```
and retry loading the dataset by forcing its redownload:
```python
dataset = load_dataset("cnn_dailymail", "3.0.0", download_mode="force_redownload")
``` | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | Hi ! Could it be because you need to free the memory used by `tarfile` by emptying the tar `members` by any chance ?
```python
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
audio_tarfile.members = [] # free memory
key += 1
```
and then you can set `DEFAULT_WRITER_BATCH_SIZE` to whatever value makes more sense for your dataset.
Let me know if the issue persists (which could happen, given that you managed to run your generator without RAM issues and using os.walk didn't solve the issue) |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 85 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
Hi ! Could it be because you need to free the memory used by `tarfile` by emptying the tar `members` by any chance ?
```python
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
audio_tarfile.members = [] # free memory
key += 1
```
and then you can set `DEFAULT_WRITER_BATCH_SIZE` to whatever value makes more sense for your dataset.
Let me know if the issue persists (which could happen, given that you managed to run your generator without RAM issues and using os.walk didn't solve the issue) | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I also run out of memory when loading `mozilla-foundation/common_voice_8_0` that also uses `tarfile` via `dl_manager.iter_archive`. There seems to be some data files that stay in memory somewhere
I don't have the issue with other compression formats like gzipped files |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 39 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I also run out of memory when loading `mozilla-foundation/common_voice_8_0` that also uses `tarfile` via `dl_manager.iter_archive`. There seems to be some data files that stay in memory somewhere
I don't have the issue with other compression formats like gzipped files | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I'm facing a similar memory leak issue when loading cv8. As you said @lhoestq
`load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)`
This issue is happening on a 32GB RAM machine.
Any updates on how to fix this? |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 34 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I'm facing a similar memory leak issue when loading cv8. As you said @lhoestq
`load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)`
This issue is happening on a 32GB RAM machine.
Any updates on how to fix this? | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I've run a memory profiler to see where's the leak comes from:
... it seems that it's related to the tarfile lib buffer reader. But I don't know why it's only happening on the huggingface script |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 37 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I've run a memory profiler to see where's the leak comes from:
... it seems that it's related to the tarfile lib buffer reader. But I don't know why it's only happening on the huggingface script | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I have the same problem when loading video into numpy.
```
yield id,{
"video": imageio.v3.imread(video_path),
"label": int(label)
}
```
Since video files are heavy, it can only processes a dozen samples before OOM. |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 33 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I have the same problem when loading video into numpy.
```
yield id,{
"video": imageio.v3.imread(video_path),
"label": int(label)
}
```
Since video files are heavy, it can only processes a dozen samples before OOM. | [
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] |
https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | For video datasets I think you can just define the max number of video that can stay in memory by adding this class attribute to your dataset builer:
```py
DEFAULT_WRITER_BATCH_SIZE = 8 # only 8 videos at a time in memory before flushing the dataset writer
``` |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 47 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
For video datasets I think you can just define the max number of video that can stay in memory by adding this class attribute to your dataset builer:
```py
DEFAULT_WRITER_BATCH_SIZE = 8 # only 8 videos at a time in memory before flushing the dataset writer
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | same thing happens for me with `load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)` on azure ml. seems to fill up `tmp` and not release that memory until OOM |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 25 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
same thing happens for me with `load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)` on azure ml. seems to fill up `tmp` and not release that memory until OOM | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I'll add that I'm encountering the same issue with
`load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.
Same for `'es'` in place of `'ceb'`. |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 20 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I'll add that I'm encountering the same issue with
`load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.
Same for `'es'` in place of `'ceb'`. | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | > I'll add that I'm encountering the same issue with
> load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train').
> Same for 'es' in place of 'ceb'.
This is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 94 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
> I'll add that I'm encountering the same issue with
> load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train').
> Same for 'es' in place of 'ceb'.
This is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | > > I'll add that I'm encountering the same issue with
> > `load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.
> > Same for `'es'` in place of `'ceb'`.
>
> This is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam
Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM! I have also tried with `Runner='Flink'` on an environment with 51GB of RAM, which also failed.
Apache Beam has tons of open tickets already - is it worth submitting one to them over this? |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 148 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
> > I'll add that I'm encountering the same issue with
> > `load_dataset('wikipedia', 'ceb', runner='DirectRunner', split='train')`.
> > Same for `'es'` in place of `'ceb'`.
>
> This is because the Apache Beam `DirectRunner` runs with the full data in memory unfortunately. Optimizing the `DirectRunner` is not in the scope of the `datasets` library, but rather in the Apache Beam project I believe. If you have memory issues with the `DirectRunner`, please consider switching to a machine with more RAM, or to distributed processing runtimes like Spark, Flink or DataFlow. There is a bit of documentation here: https://huggingface.co/docs/datasets/beam
Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM! I have also tried with `Runner='Flink'` on an environment with 51GB of RAM, which also failed.
Apache Beam has tons of open tickets already - is it worth submitting one to them over this? | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | > Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!
What, wikipedia is not even bigger than 20GB
cc @albertvillanova |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 26 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
> Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!
What, wikipedia is not even bigger than 20GB
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | > > Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!
>
> What, wikipedia is not even bigger than 20GB
>
> cc @albertvillanova
Luckily, on Colab you can watch the call stack at the bottom of the screen - much of the time and space complexity seems to come from `_parse_and_clean_wikicode()` rather than the actual download process. As far as I can tell, the script is loading the full dataset and then cleaning it all at once, which is consuming a lot of memory. |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 92 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
> > Fair enough, but this line of code crashed an AWS instance with 1024GB of RAM!
>
> What, wikipedia is not even bigger than 20GB
>
> cc @albertvillanova
Luckily, on Colab you can watch the call stack at the bottom of the screen - much of the time and space complexity seems to come from `_parse_and_clean_wikicode()` rather than the actual download process. As far as I can tell, the script is loading the full dataset and then cleaning it all at once, which is consuming a lot of memory. | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I think we are mixing many different bugs in this Issue page:
- TAR archive with audio files
- video file
- distributed parsing of Wikipedia using Apache Beam
@dan-the-meme-man may I ask you to open a separate Issue for your problem? Then I will address it. It is important to fix it because we are currently working on a Datasets enhancement to be able to provide all Wikipedias already preprocessed.
On the other hand, I think we could keep this Issue page for the original problem: TAR archive with audio files. That is not fixed yet either. |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 98 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I think we are mixing many different bugs in this Issue page:
- TAR archive with audio files
- video file
- distributed parsing of Wikipedia using Apache Beam
@dan-the-meme-man may I ask you to open a separate Issue for your problem? Then I will address it. It is important to fix it because we are currently working on a Datasets enhancement to be able to provide all Wikipedias already preprocessed.
On the other hand, I think we could keep this Issue page for the original problem: TAR archive with audio files. That is not fixed yet either. | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | Is there an update on the TAR archive issue with audio files? Happy to lend a hand in fixing this :) |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 21 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
Is there an update on the TAR archive issue with audio files? Happy to lend a hand in fixing this :) | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I found the issue with Common Voice 8 and opened a PR to fix it: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/2
Basically the `metadata` dict that contains the transcripts per audio file was continuously getting filled with bytes from `f.read()` because of this code:
```python
result = metadata[path]
result["audio"] = {"path": path, "bytes": f.read()}
```
copying the result with `result = dict(metadata[path])` fixes it: the bytes are no longer added to `metadata`
I also opened PRs to the other CV datasets |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 76 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I found the issue with Common Voice 8 and opened a PR to fix it: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/2
Basically the `metadata` dict that contains the transcripts per audio file was continuously getting filled with bytes from `f.read()` because of this code:
```python
result = metadata[path]
result["audio"] = {"path": path, "bytes": f.read()}
```
copying the result with `result = dict(metadata[path])` fixes it: the bytes are no longer added to `metadata`
I also opened PRs to the other CV datasets | [
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https://github.com/huggingface/datasets/issues/4057 | `load_dataset` consumes too much memory for audio + tar archives | I'm closing this one for now, but feel free to reopen if you encounter other memory issues with audio datasets |
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | 20 | `load_dataset` consumes too much memory for audio + tar archives
## Description
`load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists.
## Steps to reproduce the bug
Here's my implementation of `_generate_examples`:
```python
class MyDatasetBuilder(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1
...
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(_DL_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_tarfile_path": archive_path["audio_tarfile"]
},
),
]
def _generate_examples(self, audio_tarfile_path):
key = 0
with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile:
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
```
I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened.
I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times.
```python
import tarfile
def generate_examples():
audio_tarfile = tarfile.open("audios.tar", mode="r|")
key = 0
for audio_tarinfo in audio_tarfile:
audio_name = audio_tarinfo.name
audio_file_obj = audio_tarfile.extractfile(audio_tarinfo)
yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}}
key += 1
if __name__ == "__main__":
examples = generate_examples()
for example in examples:
pass
```
## Expected results
Memory consumption should be similar to the non-huggingface script.
## Actual results
Process is killed after consuming too much memory.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12
- Python version: 3.7.12
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
I'm closing this one for now, but feel free to reopen if you encounter other memory issues with audio datasets | [
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https://github.com/huggingface/datasets/issues/4056 | Unexpected behavior of _TempDirWithCustomCleanup | Hi ! Would setting TMPDIR at the beginning of your python script/session work ? I mean, even before importing transformers, datasets, etc. and using them ? I think this would be the most robust solution given any library that uses `tempfile`. I don't think we aim to support environment variables to be changed at run time | ## Describe the bug
This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side.
When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect.
## Steps to reproduce the bug
`_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected.
For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`.
## Suggestion:
I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust:
Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting
the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
| 56 | Unexpected behavior of _TempDirWithCustomCleanup
## Describe the bug
This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side.
When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect.
## Steps to reproduce the bug
`_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected.
For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`.
## Suggestion:
I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust:
Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting
the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
Hi ! Would setting TMPDIR at the beginning of your python script/session work ? I mean, even before importing transformers, datasets, etc. and using them ? I think this would be the most robust solution given any library that uses `tempfile`. I don't think we aim to support environment variables to be changed at run time | [
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https://github.com/huggingface/datasets/issues/4056 | Unexpected behavior of _TempDirWithCustomCleanup | Hi, yeah setting the environment variable before the imports / as environment variable outside is another way to fix this. I am just arguing that `datasets` already uses its own global variable to track temporary files: `_TEMP_DIR_FOR_TEMP_CACHE_FILES`, and the creation of this global variable should respect TMPDIR instead of relying on tempfile to do so. | ## Describe the bug
This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side.
When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect.
## Steps to reproduce the bug
`_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected.
For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`.
## Suggestion:
I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust:
Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting
the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
| 55 | Unexpected behavior of _TempDirWithCustomCleanup
## Describe the bug
This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side.
When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect.
## Steps to reproduce the bug
`_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected.
For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`.
## Suggestion:
I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust:
Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting
the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
Hi, yeah setting the environment variable before the imports / as environment variable outside is another way to fix this. I am just arguing that `datasets` already uses its own global variable to track temporary files: `_TEMP_DIR_FOR_TEMP_CACHE_FILES`, and the creation of this global variable should respect TMPDIR instead of relying on tempfile to do so. | [
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https://github.com/huggingface/datasets/issues/4052 | metric = metric_cls( TypeError: 'NoneType' object is not callable | Hi @klyuhang9,
I'm sorry but I can't reproduce your problem:
```python
In [2]: metric = load_metric('glue', 'rte')
Downloading builder script: 5.76kB [00:00, 2.40MB/s]
```
Could you please, retry to load the metric? Sometimes there are temporary connectivity issues.
Feel free to re-open this issue of the problem persists. | Hi, friend. I meet a problem.
When I run the code:
`metric = load_metric('glue', 'rte')`
There is a problem raising:
`metric = metric_cls(
TypeError: 'NoneType' object is not callable `
I don't know why. Thanks for your help!
| 48 | metric = metric_cls( TypeError: 'NoneType' object is not callable
Hi, friend. I meet a problem.
When I run the code:
`metric = load_metric('glue', 'rte')`
There is a problem raising:
`metric = metric_cls(
TypeError: 'NoneType' object is not callable `
I don't know why. Thanks for your help!
Hi @klyuhang9,
I'm sorry but I can't reproduce your problem:
```python
In [2]: metric = load_metric('glue', 'rte')
Downloading builder script: 5.76kB [00:00, 2.40MB/s]
```
Could you please, retry to load the metric? Sometimes there are temporary connectivity issues.
Feel free to re-open this issue of the problem persists. | [
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https://github.com/huggingface/datasets/issues/4051 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py | Hi @klyuhang9,
I'm sorry but I can't reproduce your problem:
```python
In [4]: ds = load_dataset("glue", "sst2", download_mode="force_redownload")
Downloading builder script: 28.8kB [00:00, 9.15MB/s]
Downloading metadata: 28.7kB [00:00, 10.7MB/s]
Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.78 MiB, post-processed: Unknown size, total: 11.88 MiB) to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...
Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.44M/7.44M [00:01<00:00, 4.12MB/s]
Dataset glue downloaded and prepared to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1047.96it/s]
In [5]: ds
Out[5]:
DatasetDict({
train: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 67349
})
validation: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 872
})
test: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 1821
})
})
```
Please, note that sometimes GitHub has some temporary connectivity issues. Feel free to retry and re-open this issue if the problem persists. | Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help! | 127 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help!
Hi @klyuhang9,
I'm sorry but I can't reproduce your problem:
```python
In [4]: ds = load_dataset("glue", "sst2", download_mode="force_redownload")
Downloading builder script: 28.8kB [00:00, 9.15MB/s]
Downloading metadata: 28.7kB [00:00, 10.7MB/s]
Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.78 MiB, post-processed: Unknown size, total: 11.88 MiB) to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...
Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.44M/7.44M [00:01<00:00, 4.12MB/s]
Dataset glue downloaded and prepared to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1047.96it/s]
In [5]: ds
Out[5]:
DatasetDict({
train: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 67349
})
validation: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 872
})
test: Dataset({
features: ['sentence', 'label', 'idx'],
num_rows: 1821
})
})
```
Please, note that sometimes GitHub has some temporary connectivity issues. Feel free to retry and re-open this issue if the problem persists. | [
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https://github.com/huggingface/datasets/issues/4051 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py | > Are you able to access the URL in your web browser?
Yes, with or without a VPN, we (people in China) can access the URL. And we can even use wget to download these files. We can download the pretrained language model automatically with the code.
However, we CANNOT access glue.py & metric.py automatically. Every time, it will raise ConnectionError, and we have to download datasets manually (SQuAD is extremely hard to preprocess) and replace metric.py with scipy.metrics. If this problem is solved, many Chinese will save a lot of time. | Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help! | 92 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help!
> Are you able to access the URL in your web browser?
Yes, with or without a VPN, we (people in China) can access the URL. And we can even use wget to download these files. We can download the pretrained language model automatically with the code.
However, we CANNOT access glue.py & metric.py automatically. Every time, it will raise ConnectionError, and we have to download datasets manually (SQuAD is extremely hard to preprocess) and replace metric.py with scipy.metrics. If this problem is solved, many Chinese will save a lot of time. | [
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https://github.com/huggingface/datasets/issues/4051 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py | > ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
>
> I don't know why; it is ok when I use
If you would query the question `ConnectionError: Couldn't reach` in www.baidu.com (Chinese Google, Google is banned and some people cannot access it), you will find that there are so many questions about accessing `https://raw.githubusercontent.com`. There are some solutions like adding `185.199.108.133 raw.githubusercontent.com` to `C:/windows/systen32/drives/etc/hosts`, but it is time-consuming, hard for green-hand, and invalid sometimes. | Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help! | 71 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
Hi, I meet a problem.
When I run the code:
`dataset = load_dataset('glue','sst2')`
There is a issue raising:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
I don't know why; it is ok when I use Google Chrome to view this url.
Thanks for your help!
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
>
> I don't know why; it is ok when I use
If you would query the question `ConnectionError: Couldn't reach` in www.baidu.com (Chinese Google, Google is banned and some people cannot access it), you will find that there are so many questions about accessing `https://raw.githubusercontent.com`. There are some solutions like adding `185.199.108.133 raw.githubusercontent.com` to `C:/windows/systen32/drives/etc/hosts`, but it is time-consuming, hard for green-hand, and invalid sometimes. | [
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https://github.com/huggingface/datasets/issues/4048 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset | Follow-up: I have confirmed there are no duplicate lines via `sort amazon_reviews_us_PC_v1_00.tsv | uniq -cd` after extracting the raw file. | ## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| 20 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset
## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
Follow-up: I have confirmed there are no duplicate lines via `sort amazon_reviews_us_PC_v1_00.tsv | uniq -cd` after extracting the raw file. | [
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https://github.com/huggingface/datasets/issues/4048 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset | Hi @trentonstrong, thanks for reporting!
I confirm that loading this dataset configuration throws a `NonMatchingSplitsSizesError`:
```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=350242049, num_examples=785730, dataset_name='amazon_us_reviews'), 'recorded': SplitInfo(name='train', num_bytes=3982712078, num_examples=6908554, dataset_name='amazon_us_reviews')}]
```
Also thank you for your offer to fix this. You can find information about how to update the metadata JSON file here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata
```shell
datasets-cli test datasets/amazon_us_reviews --save_infos --all_configs
```
Please, feel free to open a PR with this fix. And do not hesitate to ping me if you need any help. | ## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
| 80 | Split size error on `amazon_us_reviews` / `PC_v1_00` dataset
## Describe the bug
When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m.
Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata.
Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first.
## Steps to reproduce the bug
```python
load_dataset('amazon_us_reviews', 'PC_v1_00')
```
## Expected results
Dataset is downloaded and extracted successfully.
## Actual results
An split size exception is thrown.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
Hi @trentonstrong, thanks for reporting!
I confirm that loading this dataset configuration throws a `NonMatchingSplitsSizesError`:
```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=350242049, num_examples=785730, dataset_name='amazon_us_reviews'), 'recorded': SplitInfo(name='train', num_bytes=3982712078, num_examples=6908554, dataset_name='amazon_us_reviews')}]
```
Also thank you for your offer to fix this. You can find information about how to update the metadata JSON file here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata
```shell
datasets-cli test datasets/amazon_us_reviews --save_infos --all_configs
```
Please, feel free to open a PR with this fix. And do not hesitate to ping me if you need any help. | [
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https://github.com/huggingface/datasets/issues/4047 | Dataset.unique(column: str) -> ArrowNotImplementedError | Hi @orkenstein, thanks for reporting.
Please note that for this case, our `datasets` library uses under the hood the Apache Arrow `unique` function: https://arrow.apache.org/docs/python/generated/pyarrow.compute.unique.html#pyarrow.compute.unique
And currently the Apache Arrow `unique` function is only implemented for these input types (see info in their [docs](https://arrow.apache.org/docs/cpp/compute.html#array-wise-vector-functions)): Boolean, Null, Numeric, Temporal, Binary- and String-like.
However, the data types of the `wikiann` dataset are all `list<item: string>` (see its [dataset card](https://huggingface.co/datasets/wikiann#data-fields)), and thus, not yet supported by the Apache Arrow `unique` function. | ## Describe the bug
I'm trying to use `unique()` function, but it fails
## Steps to reproduce the bug
1. Get dataset
2. Call `unique`
3. Error
# Sample code to reproduce the bug
```python
!pip show datasets
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
dataset['train'].column_names
dataset['train'].unique(dataset['train'].column_names[0])
```
## Expected results
It would be nice to actually see unique items
## Actual results
Error:
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
[<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>()
6
7 dataset['train'].column_names
----> 8 dataset['train'].unique(dataset['train'].column_names[0])
5 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>])
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Google Collab
- Python version: 3.7.13
- PyArrow version: 6.0.1
| 77 | Dataset.unique(column: str) -> ArrowNotImplementedError
## Describe the bug
I'm trying to use `unique()` function, but it fails
## Steps to reproduce the bug
1. Get dataset
2. Call `unique`
3. Error
# Sample code to reproduce the bug
```python
!pip show datasets
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
dataset['train'].column_names
dataset['train'].unique(dataset['train'].column_names[0])
```
## Expected results
It would be nice to actually see unique items
## Actual results
Error:
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
[<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>()
6
7 dataset['train'].column_names
----> 8 dataset['train'].unique(dataset['train'].column_names[0])
5 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>])
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Google Collab
- Python version: 3.7.13
- PyArrow version: 6.0.1
Hi @orkenstein, thanks for reporting.
Please note that for this case, our `datasets` library uses under the hood the Apache Arrow `unique` function: https://arrow.apache.org/docs/python/generated/pyarrow.compute.unique.html#pyarrow.compute.unique
And currently the Apache Arrow `unique` function is only implemented for these input types (see info in their [docs](https://arrow.apache.org/docs/cpp/compute.html#array-wise-vector-functions)): Boolean, Null, Numeric, Temporal, Binary- and String-like.
However, the data types of the `wikiann` dataset are all `list<item: string>` (see its [dataset card](https://huggingface.co/datasets/wikiann#data-fields)), and thus, not yet supported by the Apache Arrow `unique` function. | [
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https://github.com/huggingface/datasets/issues/4047 | Dataset.unique(column: str) -> ArrowNotImplementedError | As a workaround solution you can use pandas:
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en', split='train')
df = dataset.to_pandas()
unique_df = df[~df.tokens.apply(tuple).duplicated()] # from https://stackoverflow.com/a/46958336/17517845
```
Note that pandas loads the dataset in memory (this one is small so it's fine). | ## Describe the bug
I'm trying to use `unique()` function, but it fails
## Steps to reproduce the bug
1. Get dataset
2. Call `unique`
3. Error
# Sample code to reproduce the bug
```python
!pip show datasets
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
dataset['train'].column_names
dataset['train'].unique(dataset['train'].column_names[0])
```
## Expected results
It would be nice to actually see unique items
## Actual results
Error:
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
[<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>()
6
7 dataset['train'].column_names
----> 8 dataset['train'].unique(dataset['train'].column_names[0])
5 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>])
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Google Collab
- Python version: 3.7.13
- PyArrow version: 6.0.1
| 43 | Dataset.unique(column: str) -> ArrowNotImplementedError
## Describe the bug
I'm trying to use `unique()` function, but it fails
## Steps to reproduce the bug
1. Get dataset
2. Call `unique`
3. Error
# Sample code to reproduce the bug
```python
!pip show datasets
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en')
dataset['train'].column_names
dataset['train'].unique(dataset['train'].column_names[0])
```
## Expected results
It would be nice to actually see unique items
## Actual results
Error:
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
[<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>()
6
7 dataset['train'].column_names
----> 8 dataset['train'].unique(dataset['train'].column_names[0])
5 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>])
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Google Collab
- Python version: 3.7.13
- PyArrow version: 6.0.1
As a workaround solution you can use pandas:
```python
from datasets import load_dataset
dataset = load_dataset('wikiann', 'en', split='train')
df = dataset.to_pandas()
unique_df = df[~df.tokens.apply(tuple).duplicated()] # from https://stackoverflow.com/a/46958336/17517845
```
Note that pandas loads the dataset in memory (this one is small so it's fine). | [
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https://github.com/huggingface/datasets/issues/4041 | Add support for IIIF in datasets | Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of "using IIIF through datasets scripts" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle bad URLs in `map` by returning `None`. Plus, we can add a `Dataset Preprocessing` section with the code that explains this approach to the card of such datasets. WDYT?
> currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
This is why (currently) adding a new feature type would be overkill, IMO.
| This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred.
## What is [IIIF](https://iiif.io/)?
IIIF (International Image Interoperability Framework)
> is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions.
The tl;dr is that IIIF provides various specifications for implementing useful functionality for:
- Institutions to make available images for various use cases
- Users to have a consistent way of interacting/requesting these images
- For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF).
Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/
## IIIF APIs
IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/)
### IIIF Image API
The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL:
```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}```
A concrete example of this:
```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg```
As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:
We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`
We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`
A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size
## Why would/could this be useful for datasets?
There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows:
- images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller
- can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use.
- options for quality, rotation, the format can all be encoded in the URL request.
These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact.
## What could this look like in datasets?
I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach.
### Use through datasets scripts
Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script:
```python
ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg")
```
This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script.
### Support through dataset scripts (with some datasets support)
This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like:
```python
features = {"label": ClassLabel(names=['dog','cat']),
"url": datasets.IIIFURL()}
```
inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset.
### Other possible integrations
Some other possible pseudocode ways that a user could interact with IIIF URLs:
The ability to cast to an `IIIFImage` feature type:
```
ds.cast_column('url', IIIFImage, download=False)
```
The ability to specify some options associated with IIIF urls.
```
ds = ds.set_iiif_options(column='url', size="250,250")
```
I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways.
## prerequisite requirements
There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support:
### support for handling failed images loaded via a URL (or a specific IIIFImage feature).
Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs.
```python
from datasets import Dataset
import datasets
urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3
urls.append("badurl.com/image.jpg")
data = {"url":urls}
ds = Dataset.from_dict(data)
ds = ds.cast_column('url', datasets.Image())
ds[3]['url']
```
returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this.
### Caching support
Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs.
### Support for Parsing IIIF URLs
This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share.
## Why it might not be worthwhile/suitable for datasets
There are some reasons that this might not be worth implementing:
- currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
- It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble.
- The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from.
Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets.
## Suggested next steps:
I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.
| 145 | Add support for IIIF in datasets
This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred.
## What is [IIIF](https://iiif.io/)?
IIIF (International Image Interoperability Framework)
> is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions.
The tl;dr is that IIIF provides various specifications for implementing useful functionality for:
- Institutions to make available images for various use cases
- Users to have a consistent way of interacting/requesting these images
- For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF).
Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/
## IIIF APIs
IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/)
### IIIF Image API
The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL:
```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}```
A concrete example of this:
```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg```
As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:
We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`
We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`
A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size
## Why would/could this be useful for datasets?
There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows:
- images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller
- can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use.
- options for quality, rotation, the format can all be encoded in the URL request.
These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact.
## What could this look like in datasets?
I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach.
### Use through datasets scripts
Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script:
```python
ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg")
```
This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script.
### Support through dataset scripts (with some datasets support)
This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like:
```python
features = {"label": ClassLabel(names=['dog','cat']),
"url": datasets.IIIFURL()}
```
inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset.
### Other possible integrations
Some other possible pseudocode ways that a user could interact with IIIF URLs:
The ability to cast to an `IIIFImage` feature type:
```
ds.cast_column('url', IIIFImage, download=False)
```
The ability to specify some options associated with IIIF urls.
```
ds = ds.set_iiif_options(column='url', size="250,250")
```
I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways.
## prerequisite requirements
There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support:
### support for handling failed images loaded via a URL (or a specific IIIFImage feature).
Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs.
```python
from datasets import Dataset
import datasets
urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3
urls.append("badurl.com/image.jpg")
data = {"url":urls}
ds = Dataset.from_dict(data)
ds = ds.cast_column('url', datasets.Image())
ds[3]['url']
```
returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this.
### Caching support
Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs.
### Support for Parsing IIIF URLs
This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share.
## Why it might not be worthwhile/suitable for datasets
There are some reasons that this might not be worth implementing:
- currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
- It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble.
- The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from.
Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets.
## Suggested next steps:
I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.
Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of "using IIIF through datasets scripts" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle bad URLs in `map` by returning `None`. Plus, we can add a `Dataset Preprocessing` section with the code that explains this approach to the card of such datasets. WDYT?
> currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
This is why (currently) adding a new feature type would be overkill, IMO.
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https://github.com/huggingface/datasets/issues/4037 | Error while building documentation | After some investigation, maybe the bug is in `doc-builder`.
I've opened an issue there:
- huggingface/doc-builder#160 | ## Describe the bug
Documentation building is failing:
- https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true
```
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct.
```
| 16 | Error while building documentation
## Describe the bug
Documentation building is failing:
- https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true
```
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct.
```
After some investigation, maybe the bug is in `doc-builder`.
I've opened an issue there:
- huggingface/doc-builder#160 | [
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https://github.com/huggingface/datasets/issues/4031 | Cannot load the dataset conll2012_ontonotesv5 | Hi @cathyxl, thanks for reporting.
Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well):
- #4002
That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:
- installing `datasets` from our GitHub repo:
```bash
pip install git+https://github.com/huggingface/datasets#egg=datasets
```
- forcing the data files to be redownloaded
```python
ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload")
```
Feel free to re-open this issue if the problem persists. | ## Describe the bug
Cannot load the dataset conll2012_ontonotesv5
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test")
print(dataset)
```
## Expected results
The datasets should be downloaded successfully
## Actual results
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
| 82 | Cannot load the dataset conll2012_ontonotesv5
## Describe the bug
Cannot load the dataset conll2012_ontonotesv5
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test")
print(dataset)
```
## Expected results
The datasets should be downloaded successfully
## Actual results
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
Hi @cathyxl, thanks for reporting.
Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well):
- #4002
That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:
- installing `datasets` from our GitHub repo:
```bash
pip install git+https://github.com/huggingface/datasets#egg=datasets
```
- forcing the data files to be redownloaded
```python
ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload")
```
Feel free to re-open this issue if the problem persists. | [
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https://github.com/huggingface/datasets/issues/4029 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold | Hi ! You can access the faiss index with
```python
faiss_index = my_dataset.get_index("my_index_name").faiss_index
```
and then do whatever you want with it, e.g. query it using range_search:
```python
threshold = 0.95
limits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold)
texts = dataset[indices]
``` | **Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
| 41 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
**Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
Hi ! You can access the faiss index with
```python
faiss_index = my_dataset.get_index("my_index_name").faiss_index
```
and then do whatever you want with it, e.g. query it using range_search:
```python
threshold = 0.95
limits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold)
texts = dataset[indices]
``` | [
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https://github.com/huggingface/datasets/issues/4029 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold | wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it)
which type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well? | **Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
| 76 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
**Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it)
which type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well? | [
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https://github.com/huggingface/datasets/issues/4029 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold | `Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index`
Here is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index | **Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
| 44 | Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
**Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
`Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index`
Here is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index | [
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https://github.com/huggingface/datasets/issues/4027 | ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme' | Hi, @MoritzLaurer, thanks for reporting.
Normally this is due to a mismatch between the versions of your Elasticsearch client and server:
- your ES client is passing only keyword arguments to your ES server
- whereas your ES server expects a positional argument called 'scheme'
In order to fix this, you should align the major versions of both Elasticsearch client and server.
You can have more info:
- on this other issue page: https://github.com/huggingface/datasets/issues/3956#issuecomment-1072115173
- Elasticsearch client docs: https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/overview.html#_compatibility
Feel free to re-open this issue if the problem persists.
Duplicate of:
- #3956 | ## Describe the bug
I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch
```
from datasets import load_dataset
squad = load_dataset('crime_and_punish', split='train[:1000]')
```
When I run the line:
`squad.add_elasticsearch_index("context", host="localhost", port="9200")`
I get the error:
`TypeError: __init__() missing 1 required positional argument: 'scheme'`
## Expected results
No error message
## Actual results
```
TypeError Traceback (most recent call last)
[<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>()
1 import elasticsearch
----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200")
6 frames
[/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux, Google Colab
- Python version: Google Colab (probably 3.7)
- PyArrow version: ?
| 93 | ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme'
## Describe the bug
I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch
```
from datasets import load_dataset
squad = load_dataset('crime_and_punish', split='train[:1000]')
```
When I run the line:
`squad.add_elasticsearch_index("context", host="localhost", port="9200")`
I get the error:
`TypeError: __init__() missing 1 required positional argument: 'scheme'`
## Expected results
No error message
## Actual results
```
TypeError Traceback (most recent call last)
[<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>()
1 import elasticsearch
----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200")
6 frames
[/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux, Google Colab
- Python version: Google Colab (probably 3.7)
- PyArrow version: ?
Hi, @MoritzLaurer, thanks for reporting.
Normally this is due to a mismatch between the versions of your Elasticsearch client and server:
- your ES client is passing only keyword arguments to your ES server
- whereas your ES server expects a positional argument called 'scheme'
In order to fix this, you should align the major versions of both Elasticsearch client and server.
You can have more info:
- on this other issue page: https://github.com/huggingface/datasets/issues/3956#issuecomment-1072115173
- Elasticsearch client docs: https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/overview.html#_compatibility
Feel free to re-open this issue if the problem persists.
Duplicate of:
- #3956 | [
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https://github.com/huggingface/datasets/issues/4027 | ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme' | 1. Check elasticsearch version
```
import elasticsearch
print(elasticsearch.__version__)
```
Ex: 7.9.1
2. Uninstall current elasticsearch package
`pip uninstall elasticsearch`
3. Install elasticsearch 7.9.1 package
`pip install elasticsearch==7.9.1` | ## Describe the bug
I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch
```
from datasets import load_dataset
squad = load_dataset('crime_and_punish', split='train[:1000]')
```
When I run the line:
`squad.add_elasticsearch_index("context", host="localhost", port="9200")`
I get the error:
`TypeError: __init__() missing 1 required positional argument: 'scheme'`
## Expected results
No error message
## Actual results
```
TypeError Traceback (most recent call last)
[<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>()
1 import elasticsearch
----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200")
6 frames
[/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux, Google Colab
- Python version: Google Colab (probably 3.7)
- PyArrow version: ?
| 27 | ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme'
## Describe the bug
I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch
```
from datasets import load_dataset
squad = load_dataset('crime_and_punish', split='train[:1000]')
```
When I run the line:
`squad.add_elasticsearch_index("context", host="localhost", port="9200")`
I get the error:
`TypeError: __init__() missing 1 required positional argument: 'scheme'`
## Expected results
No error message
## Actual results
```
TypeError Traceback (most recent call last)
[<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>()
1 import elasticsearch
----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200")
6 frames
[/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host)
209 options["path_prefix"] = options.pop("url_prefix")
210
--> 211 return NodeConfig(**options) # type: ignore
212
213
TypeError: __init__() missing 1 required positional argument: 'scheme'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux, Google Colab
- Python version: Google Colab (probably 3.7)
- PyArrow version: ?
1. Check elasticsearch version
```
import elasticsearch
print(elasticsearch.__version__)
```
Ex: 7.9.1
2. Uninstall current elasticsearch package
`pip uninstall elasticsearch`
3. Install elasticsearch 7.9.1 package
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https://github.com/huggingface/datasets/issues/4015 | Can not correctly parse the classes with imagefolder | I found that the problem arises because the image files in my folder are actually symbolic links (for my own reasons). After modifications, the classes can now be correctly parsed. Therefore, I close this issue. | ## Describe the bug
I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect.
## Steps to reproduce the bug
I organized my dataset (ImageNet) in the following structure:
```
- imagenet/
- train/
- n01440764/
- ILSVRC2012_val_00000293.jpg
- ......
- n01695060/
- ......
- val/
- n01440764/
- n01695060/
- ......
```
At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as:
```
from datasets import load_dataset
data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'}
ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification")
```
but it resulted following error (I mask my personal path as <PERSONAL_PATH>):
```
FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
```
Next, I followed a recent issue #3960 to load data as:
```
from datasets import load_dataset
data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']}
ds = load_dataset("imagefolder", data_files=data_files, task="image-classification")
```
and the data can be loaded without error as: (I copy val folder to train folder for illustration)
```
>>> ds
DatasetDict({
train: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
val: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
})
```
However, the parsed classes is wrong (should be 1000 classes):
```
>>> ds["train"].features
{'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)}
```
## Expected results
I expect that the "labels" in ds["train"].features should contain 1000 classes.
## Actual results
The "labels" in ds["train"].features contains only 1 wrong class.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu 18.04
- Python version: Python 3.7.12
- PyArrow version: 7.0.0
| 35 | Can not correctly parse the classes with imagefolder
## Describe the bug
I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect.
## Steps to reproduce the bug
I organized my dataset (ImageNet) in the following structure:
```
- imagenet/
- train/
- n01440764/
- ILSVRC2012_val_00000293.jpg
- ......
- n01695060/
- ......
- val/
- n01440764/
- n01695060/
- ......
```
At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as:
```
from datasets import load_dataset
data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'}
ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification")
```
but it resulted following error (I mask my personal path as <PERSONAL_PATH>):
```
FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main
```
Next, I followed a recent issue #3960 to load data as:
```
from datasets import load_dataset
data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']}
ds = load_dataset("imagefolder", data_files=data_files, task="image-classification")
```
and the data can be loaded without error as: (I copy val folder to train folder for illustration)
```
>>> ds
DatasetDict({
train: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
val: Dataset({
features: ['image', 'labels'],
num_rows: 50000
})
})
```
However, the parsed classes is wrong (should be 1000 classes):
```
>>> ds["train"].features
{'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)}
```
## Expected results
I expect that the "labels" in ds["train"].features should contain 1000 classes.
## Actual results
The "labels" in ds["train"].features contains only 1 wrong class.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Ubuntu 18.04
- Python version: Python 3.7.12
- PyArrow version: 7.0.0
I found that the problem arises because the image files in my folder are actually symbolic links (for my own reasons). After modifications, the classes can now be correctly parsed. Therefore, I close this issue. | [
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https://github.com/huggingface/datasets/issues/4013 | Cannot preview "hazal/Turkish-Biomedical-corpus-trM" | Hi @hazalturkmen, thanks for reporting.
Note that your dataset repository does not contain any loading script; it only contains a data file named `tr_article_2`.
When there is no loading script but only data files, the `datasets` library tries to infer how to load the data by looking at the data file extensions. However, your data file does not have any extension.
Note that current supported data file extensions are: 'csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'.
You have more info on our docs: [How to share a dataset](https://huggingface.co/docs/datasets/share). | ## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM'
**Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM*
*I cannot see the dataset preview.*
```
Server Error
Status code: 400
Exception: HTTPError
Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true
```
Am I the one who added this dataset ? Yes
| 151 | Cannot preview "hazal/Turkish-Biomedical-corpus-trM"
## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM'
**Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM*
*I cannot see the dataset preview.*
```
Server Error
Status code: 400
Exception: HTTPError
Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true
```
Am I the one who added this dataset ? Yes
Hi @hazalturkmen, thanks for reporting.
Note that your dataset repository does not contain any loading script; it only contains a data file named `tr_article_2`.
When there is no loading script but only data files, the `datasets` library tries to infer how to load the data by looking at the data file extensions. However, your data file does not have any extension.
Note that current supported data file extensions are: 'csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'.
You have more info on our docs: [How to share a dataset](https://huggingface.co/docs/datasets/share). | [
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https://github.com/huggingface/datasets/issues/4007 | set_format does not work with multi dimension tensor | Hi! Use the `ArrayXD` feature type (where X is the number of dimensions) to get correctly formated tensors. So in your case, define the dataset as follows :
```python
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}, features=Features({"A": Array2D(shape=(2, 2), dtype="float32")}))
```
| ## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0 | 39 | set_format does not work with multi dimension tensor
## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
Hi! Use the `ArrayXD` feature type (where X is the number of dimensions) to get correctly formated tensors. So in your case, define the dataset as follows :
```python
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}, features=Features({"A": Array2D(shape=(2, 2), dtype="float32")}))
```
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https://github.com/huggingface/datasets/issues/4007 | set_format does not work with multi dimension tensor | Hi @mariosasko I'm facing the same issue and the only work around I've found so far is to convert my `DatasetDict` to a dictionary and then create new objects with `Dataset.from_dict`.
```
dataset = load_dataset("my_dataset.py")
dataset = dataset.map(lambda example: blabla(example))
dict_dataset_test = dataset["test"].to_dict()
...
dataset_test = Dataset.from_dict(dict_dataset_test, features=Features(features))
```
However, converting a `Dataset` object to a dict takes quite a lot of time and memory... Is there a way to directly create an `Array2D` without having to transform the original `Dataset` to a dict? | ## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0 | 84 | set_format does not work with multi dimension tensor
## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
Hi @mariosasko I'm facing the same issue and the only work around I've found so far is to convert my `DatasetDict` to a dictionary and then create new objects with `Dataset.from_dict`.
```
dataset = load_dataset("my_dataset.py")
dataset = dataset.map(lambda example: blabla(example))
dict_dataset_test = dataset["test"].to_dict()
...
dataset_test = Dataset.from_dict(dict_dataset_test, features=Features(features))
```
However, converting a `Dataset` object to a dict takes quite a lot of time and memory... Is there a way to directly create an `Array2D` without having to transform the original `Dataset` to a dict? | [
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https://github.com/huggingface/datasets/issues/4007 | set_format does not work with multi dimension tensor | Hi! Yes, you can directly pass the `Features` dictionary as `features` in `map` to cast the column to `Array2D`:
```python
dataset = dataset.map(lambda example: blabla(example), features=Features(features))
```
Or you can use `cast` after `map` to do that:
```python
dataset = dataset.map(lambda example: blabla(example))
dataset = dataset.cast(Features(features))
``` | ## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0 | 47 | set_format does not work with multi dimension tensor
## Describe the bug
set_format only transforms the last dimension of a multi-dimension list to tensor
## Steps to reproduce the bug
```python
import torch
from datasets import Dataset
ds = Dataset.from_dict({"A": [torch.rand((2, 2))]})
# ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result
ds = ds.with_format("torch")
print(ds[0])
```
## Expected results
```
{'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]}
```
## Actual results
```
{'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- datasets version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
Hi! Yes, you can directly pass the `Features` dictionary as `features` in `map` to cast the column to `Array2D`:
```python
dataset = dataset.map(lambda example: blabla(example), features=Features(features))
```
Or you can use `cast` after `map` to do that:
```python
dataset = dataset.map(lambda example: blabla(example))
dataset = dataset.cast(Features(features))
``` | [
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] |
https://github.com/huggingface/datasets/issues/4005 | Yelp not working | I don't think it's an issue with the dataset-viewer. Maybe @lhoestq or @albertvillanova could confirm.
```python
>>> from datasets import load_dataset, DownloadMode
>>> import itertools
>>> # without streaming
>>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD)
Downloading builder script: 4.39kB [00:00, 5.97MB/s]
Downloading metadata: 2.13kB [00:00, 3.14MB/s]
Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /home/slesage/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.10k/1.10k [00:00<00:00, 1.39MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets/src/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/slesage/hf/datasets/src/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']
>>> # with streaming
>>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD, streaming=True)
Downloading builder script: 4.39kB [00:00, 5.53MB/s]
Downloading metadata: 2.13kB [00:00, 3.14MB/s]
Traceback (most recent call last):
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 375, in _info
await _file_info(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 736, in _file_info
r.raise_for_status()
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/aiohttp/client_reqrep.py", line 1000, in raise_for_status
raise ClientResponseError(
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://doc-0g-bs-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/gklhpdq1arj8v15qrg7ces34a8c3413d/1648144575000/07511006523564980941/*/0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0?e=download')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets/src/datasets/load.py", line 1677, in load_dataset
return builder_instance.as_streaming_dataset(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 906, in as_streaming_dataset
splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/yelp_review_full/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43/yelp_review_full.py", line 102, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 800, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 778, in extract
urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
File "/home/slesage/hf/datasets/src/datasets/utils/py_utils.py", line 306, in map_nested
return function(data_struct)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 783, in _extract
protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 372, in _get_extraction_protocol
with fsspec.open(urlpath, **kwargs) as f:
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/core.py", line 102, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/spec.py", line 978, in open
f = self._open(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 335, in _open
size = size or self.info(path, **kwargs)["size"]
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 88, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 69, in sync
raise result[0]
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 25, in _runner
result[0] = await coro
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 388, in _info
raise FileNotFoundError(url) from exc
FileNotFoundError: https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0&confirm=t
```
And this is before even trying to access the rows with
```python
>>> rows = list(itertools.islice(dataset, 100))
>>> rows = list(dataset.take(100))
``` | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 383 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
I don't think it's an issue with the dataset-viewer. Maybe @lhoestq or @albertvillanova could confirm.
```python
>>> from datasets import load_dataset, DownloadMode
>>> import itertools
>>> # without streaming
>>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD)
Downloading builder script: 4.39kB [00:00, 5.97MB/s]
Downloading metadata: 2.13kB [00:00, 3.14MB/s]
Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /home/slesage/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.10k/1.10k [00:00<00:00, 1.39MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets/src/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/slesage/hf/datasets/src/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']
>>> # with streaming
>>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD, streaming=True)
Downloading builder script: 4.39kB [00:00, 5.53MB/s]
Downloading metadata: 2.13kB [00:00, 3.14MB/s]
Traceback (most recent call last):
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 375, in _info
await _file_info(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 736, in _file_info
r.raise_for_status()
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/aiohttp/client_reqrep.py", line 1000, in raise_for_status
raise ClientResponseError(
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://doc-0g-bs-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/gklhpdq1arj8v15qrg7ces34a8c3413d/1648144575000/07511006523564980941/*/0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0?e=download')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets/src/datasets/load.py", line 1677, in load_dataset
return builder_instance.as_streaming_dataset(
File "/home/slesage/hf/datasets/src/datasets/builder.py", line 906, in as_streaming_dataset
splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/yelp_review_full/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43/yelp_review_full.py", line 102, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 800, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 778, in extract
urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
File "/home/slesage/hf/datasets/src/datasets/utils/py_utils.py", line 306, in map_nested
return function(data_struct)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 783, in _extract
protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 372, in _get_extraction_protocol
with fsspec.open(urlpath, **kwargs) as f:
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/core.py", line 102, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/spec.py", line 978, in open
f = self._open(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 335, in _open
size = size or self.info(path, **kwargs)["size"]
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 88, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 69, in sync
raise result[0]
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 25, in _runner
result[0] = await coro
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 388, in _info
raise FileNotFoundError(url) from exc
FileNotFoundError: https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0&confirm=t
```
And this is before even trying to access the rows with
```python
>>> rows = list(itertools.islice(dataset, 100))
>>> rows = list(dataset.take(100))
``` | [
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https://github.com/huggingface/datasets/issues/4005 | Yelp not working | Yet another issue related to google drive not being nice. Most likely your IP has been banned from using their API programmatically. Do you know if we are allowed to host and redistribute the data ourselves ? | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 37 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
Yet another issue related to google drive not being nice. Most likely your IP has been banned from using their API programmatically. Do you know if we are allowed to host and redistribute the data ourselves ? | [
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https://github.com/huggingface/datasets/issues/4005 | Yelp not working | Hi,
Facing the same issue while loading the dataset:
`Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files`
Thanks | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 18 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
Hi,
Facing the same issue while loading the dataset:
`Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files`
Thanks | [
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https://github.com/huggingface/datasets/issues/4005 | Yelp not working | > Facing the same issue while loading the dataset:
>
> Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files
Thanks for reporting. I think this is the same issue. Feel free to try again later, once Google Drive stopped blocking you. You can retry by passing `download_mode="force_redownload"` to `load_dataset` | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 49 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
> Facing the same issue while loading the dataset:
>
> Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files
Thanks for reporting. I think this is the same issue. Feel free to try again later, once Google Drive stopped blocking you. You can retry by passing `download_mode="force_redownload"` to `load_dataset` | [
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https://github.com/huggingface/datasets/issues/4005 | Yelp not working | I noticed that FastAI hosts the Yelp dataset at https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz (from their catalog [here](https://course.fast.ai/datasets))
Let's update the yelp dataset script to download from there instead of Google Drive | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 28 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
I noticed that FastAI hosts the Yelp dataset at https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz (from their catalog [here](https://course.fast.ai/datasets))
Let's update the yelp dataset script to download from there instead of Google Drive | [
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https://github.com/huggingface/datasets/issues/4005 | Yelp not working | I updated the link to not use Google Drive anymore, we will do a release early next week with the updated download url of the dataset :) | ## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
| 27 | Yelp not working
## Dataset viewer issue for '*name of the dataset*'
**Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train
Doesn't work:
```
Server error
Status code: 400
Exception: Error
Message: line contains NULL
```
Am I the one who added this dataset ? No
A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
I updated the link to not use Google Drive anymore, we will do a release early next week with the updated download url of the dataset :) | [
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] |
https://github.com/huggingface/datasets/issues/4003 | ASSIN2 dataset checksum bug | Using latest code, I am still facing the issue.
```python
(base) vimos@vimosmu ➜ ~ ipython
Python 3.6.7 | packaged by conda-forge | (default, Nov 6 2019, 16:19:42)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.11.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]: from datasets import load_dataset
In [2]: load_dataset("assin2")
Downloading builder script: 4.24kB [00:00, 244kB/s]
Downloading metadata: 2.58kB [00:00, 2.19MB/s]
Using custom data configuration default
Downloading and preparing dataset assin2/default (download: 2.02 MiB, generated: 1.21 MiB, post-processed: Unknown size, total: 3.23 MiB) to /home/vimos/.cache/huggingface/datasets/assin2/default/1.0.0/8467f7acbda82f62ab960ca869dc1e96350e0e103a1ef7eaa43bbee530b80061...
Downloading data: 1.51MB [00:00, 102MB/s]
Downloading data: 116kB [00:00, 63.6MB/s]
Downloading data: 493kB [00:00, 95.8MB/s]
Downloading data files: 100%|██████████████████████████████████████████| 3/3 [00:00<00:00, 8.27it/s]
---------------------------------------------------------------------------
ExpectedMoreDownloadedFiles Traceback (most recent call last)
<ipython-input-2-b367d1ffd68e> in <module>
----> 1 load_dataset("assin2")
~/anaconda3/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1694 ignore_verifications=ignore_verifications,
1695 try_from_hf_gcs=try_from_hf_gcs,
-> 1696 use_auth_token=use_auth_token,
1697 )
1698
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
675 if verify_infos:
676 verify_checksums(
--> 677 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
678 )
679
~/anaconda3/lib/python3.6/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'https://drive.google.com/u/0/uc?id=1kb7xq6Mb3eaqe9cOAo70BaG9ypwkIqEU&export=download', 'https://drive.google.com/u/0/uc?id=1J3FpQaHxpM-FDfBUyooh-sZF-B-bM_lU&export=download', 'https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'}
``` | ## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| 264 | ASSIN2 dataset checksum bug
## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
Using latest code, I am still facing the issue.
```python
(base) vimos@vimosmu ➜ ~ ipython
Python 3.6.7 | packaged by conda-forge | (default, Nov 6 2019, 16:19:42)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.11.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]: from datasets import load_dataset
In [2]: load_dataset("assin2")
Downloading builder script: 4.24kB [00:00, 244kB/s]
Downloading metadata: 2.58kB [00:00, 2.19MB/s]
Using custom data configuration default
Downloading and preparing dataset assin2/default (download: 2.02 MiB, generated: 1.21 MiB, post-processed: Unknown size, total: 3.23 MiB) to /home/vimos/.cache/huggingface/datasets/assin2/default/1.0.0/8467f7acbda82f62ab960ca869dc1e96350e0e103a1ef7eaa43bbee530b80061...
Downloading data: 1.51MB [00:00, 102MB/s]
Downloading data: 116kB [00:00, 63.6MB/s]
Downloading data: 493kB [00:00, 95.8MB/s]
Downloading data files: 100%|██████████████████████████████████████████| 3/3 [00:00<00:00, 8.27it/s]
---------------------------------------------------------------------------
ExpectedMoreDownloadedFiles Traceback (most recent call last)
<ipython-input-2-b367d1ffd68e> in <module>
----> 1 load_dataset("assin2")
~/anaconda3/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1694 ignore_verifications=ignore_verifications,
1695 try_from_hf_gcs=try_from_hf_gcs,
-> 1696 use_auth_token=use_auth_token,
1697 )
1698
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
604 if not downloaded_from_gcs:
605 self._download_and_prepare(
--> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
607 )
608 # Sync info
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1102
1103 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1105
1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
675 if verify_infos:
676 verify_checksums(
--> 677 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
678 )
679
~/anaconda3/lib/python3.6/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'https://drive.google.com/u/0/uc?id=1kb7xq6Mb3eaqe9cOAo70BaG9ypwkIqEU&export=download', 'https://drive.google.com/u/0/uc?id=1J3FpQaHxpM-FDfBUyooh-sZF-B-bM_lU&export=download', 'https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'}
``` | [
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https://github.com/huggingface/datasets/issues/4003 | ASSIN2 dataset checksum bug | That's true. Steps to reproduce the bug on Google Colab:
```
git clone https://github.com/huggingface/datasets.git
cd datasets
pip install -e .
python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])"
```
However the dataset will load without any problems if you just install version 2.0.0:
```
pip install datasets
python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])"
```
Any thoughts @lhoestq ? | ## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| 58 | ASSIN2 dataset checksum bug
## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
That's true. Steps to reproduce the bug on Google Colab:
```
git clone https://github.com/huggingface/datasets.git
cd datasets
pip install -e .
python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])"
```
However the dataset will load without any problems if you just install version 2.0.0:
```
pip install datasets
python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])"
```
Any thoughts @lhoestq ? | [
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https://github.com/huggingface/datasets/issues/4003 | ASSIN2 dataset checksum bug | Right indeed ! Let me open a PR to fix this.
The dataset_infos.json file that stores some metadata about the dataset to download (and is used to verify it was correctly downloaded) hasn't been updated correctly | ## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| 36 | ASSIN2 dataset checksum bug
## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
Right indeed ! Let me open a PR to fix this.
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https://github.com/huggingface/datasets/issues/4003 | ASSIN2 dataset checksum bug | Not sure what the status of this is, but personally I am still getting this error, with glue. | ## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
| 18 | ASSIN2 dataset checksum bug
## Describe the bug
Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2).
`NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`.
Similar to #3952 , #3942 , #3941 , etc.
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
[<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>()
----> 1 load_dataset('assin2')
4 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download']
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("assin2")
```
## Expected results
Load the dataset.
## Actual results
The dataset won't load.
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Google Colab
- Python version: 3.7.12
- PyArrow version: 6.0.1
Not sure what the status of this is, but personally I am still getting this error, with glue. | [
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https://github.com/huggingface/datasets/issues/4001 | How to use generate this multitask dataset for SQUAD? I am getting a value error. | Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues. | ## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
| 32 | How to use generate this multitask dataset for SQUAD? I am getting a value error.
## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues. | [
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https://github.com/huggingface/datasets/issues/4001 | How to use generate this multitask dataset for SQUAD? I am getting a value error. | But I request you to please fix the same in the dataset hub explorer as well... | ## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
| 16 | How to use generate this multitask dataset for SQUAD? I am getting a value error.
## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
But I request you to please fix the same in the dataset hub explorer as well... | [
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https://github.com/huggingface/datasets/issues/4000 | load_dataset error: sndfile library not found | Hi @i-am-neo,
The audio support is an extra feature of `datasets` and therefore it must be installed as an additional optional dependency:
```shell
pip install datasets[audio]
```
Additionally, for specific MP3 support (which is not the case for AMI dataset, that contains WAV audio files), there is another third-party dependency on `torchaudio`.
You have all the information in our docs: https://huggingface.co/docs/datasets/audio_process#installation | ## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| 61 | load_dataset error: sndfile library not found
## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
Hi @i-am-neo,
The audio support is an extra feature of `datasets` and therefore it must be installed as an additional optional dependency:
```shell
pip install datasets[audio]
```
Additionally, for specific MP3 support (which is not the case for AMI dataset, that contains WAV audio files), there is another third-party dependency on `torchaudio`.
You have all the information in our docs: https://huggingface.co/docs/datasets/audio_process#installation | [
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] |
https://github.com/huggingface/datasets/issues/4000 | load_dataset error: sndfile library not found | Thanks @albertvillanova . Unfortunately the error persists after installing ```datasets[audio]```. Can you direct towards a solution?
```
pip3 install datasets[audio]
```
### log
Requirement already satisfied: datasets[audio] in ./.virtualenvs/hubert/lib/python3.7/site-packages (1.18.3)
Requirement already satisfied: numpy>=1.17 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (1.21.5)
Requirement already satisfied: xxhash in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (3.0.0)
Requirement already satisfied: fsspec[http]>=2021.05.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (2022.2.0)
Requirement already satisfied: dill in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.3.4)
Requirement already satisfied: pandas in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (1.3.5)
Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.4.0)
Requirement already satisfied: packaging in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (21.3)
Requirement already satisfied: multiprocess in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.70.12.2)
Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (7.0.0)
Requirement already satisfied: tqdm>=4.62.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (4.63.1)
Requirement already satisfied: aiohttp in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (3.8.1)
Requirement already satisfied: importlib-metadata in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (4.11.3)
Requirement already satisfied: requests>=2.19.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (2.27.1)
Requirement already satisfied: librosa in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.9.1)
Requirement already satisfied: pyyaml in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (6.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (4.1.1)
Requirement already satisfied: filelock in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (3.6.0)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from packaging->datasets[audio]) (3.0.7)
Requirement already satisfied: idna<4,>=2.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (3.3)
Requirement already satisfied: certifi>=2017.4.17 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (2021.10.8)
Requirement already satisfied: charset-normalizer~=2.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (2.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (1.26.9)
Requirement already satisfied: attrs>=17.3.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (21.4.0)
Requirement already satisfied: frozenlist>=1.1.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.3.0)
Requirement already satisfied: aiosignal>=1.1.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.2.0)
Requirement already satisfied: yarl<2.0,>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.7.2)
Requirement already satisfied: asynctest==0.13.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (0.13.0)
Requirement already satisfied: multidict<7.0,>=4.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (6.0.2)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (4.0.2)
Requirement already satisfied: zipp>=0.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from importlib-metadata->datasets[audio]) (3.7.0)
Requirement already satisfied: decorator>=4.0.10 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (5.1.1)
Requirement already satisfied: soundfile>=0.10.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.10.3.post1)
Requirement already satisfied: numba>=0.45.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.55.1)
Requirement already satisfied: pooch>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.6.0)
Requirement already satisfied: resampy>=0.2.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.2.2)
Requirement already satisfied: audioread>=2.1.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (2.1.9)
Requirement already satisfied: joblib>=0.14 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.1.0)
Requirement already satisfied: scipy>=1.2.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.7.3)
Requirement already satisfied: scikit-learn>=0.19.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.0.2)
Requirement already satisfied: python-dateutil>=2.7.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pandas->datasets[audio]) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pandas->datasets[audio]) (2022.1)
Requirement already satisfied: setuptools in ./.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa->datasets[audio]) (60.10.0)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa->datasets[audio]) (0.38.0)
Requirement already satisfied: appdirs>=1.3.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa->datasets[audio]) (1.4.4)
Requirement already satisfied: six>=1.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets[audio]) (1.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from scikit-learn>=0.19.1->librosa->datasets[audio]) (3.1.0)
Requirement already satisfied: cffi>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from soundfile>=0.10.2->librosa->datasets[audio]) (1.15.0)
Requirement already satisfied: pycparser in ./.virtualenvs/hubert/lib/python3.7/site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa->datasets[audio]) (2.21)
### reload
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
### log
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 33542.59it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:06<00:00, 22.28it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 21558.39it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2996.41it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 23431.87it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2697.52it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
### just to double-check as per your docs
```
pip3 install librosa torchaudio
```
### logs
Requirement already satisfied: librosa in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (0.9.1)
Requirement already satisfied: torchaudio in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (0.11.0+cu113)
Requirement already satisfied: audioread>=2.1.5 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (2.1.9)
Requirement already satisfied: joblib>=0.14 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.1.0)
Requirement already satisfied: packaging>=20.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (21.3)
Requirement already satisfied: scikit-learn>=0.19.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.0.2)
Requirement already satisfied: scipy>=1.2.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.7.3)
Requirement already satisfied: decorator>=4.0.10 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (5.1.1)
Requirement already satisfied: resampy>=0.2.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.2.2)
Requirement already satisfied: pooch>=1.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.6.0)
Requirement already satisfied: numpy>=1.17.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.21.5)
Requirement already satisfied: soundfile>=0.10.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.10.3.post1)
Requirement already satisfied: numba>=0.45.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.55.1)
Requirement already satisfied: torch==1.11.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from torchaudio) (1.11.0+cu113)
Requirement already satisfied: typing-extensions in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from torch==1.11.0->torchaudio) (4.1.1)
Requirement already satisfied: setuptools in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa) (60.10.0)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa) (0.38.0)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from packaging>=20.0->librosa) (3.0.7)
Requirement already satisfied: requests>=2.19.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa) (2.27.1)
Requirement already satisfied: appdirs>=1.3.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa) (1.4.4)
Requirement already satisfied: six>=1.3 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from resampy>=0.2.2->librosa) (1.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from scikit-learn>=0.19.1->librosa) (3.1.0)
Requirement already satisfied: cffi>=1.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from soundfile>=0.10.2->librosa) (1.15.0)
Requirement already satisfied: pycparser in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21)
Requirement already satisfied: charset-normalizer~=2.0.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.0.12)
Requirement already satisfied: certifi>=2017.4.17 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2021.10.8)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.9)
Requirement already satisfied: idna<4,>=2.5 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.3)
### try loading again
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
### same error
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 33542.59it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:06<00:00, 22.28it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 21558.39it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2996.41it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 23431.87it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2697.52it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
| ## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| 969 | load_dataset error: sndfile library not found
## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
Thanks @albertvillanova . Unfortunately the error persists after installing ```datasets[audio]```. Can you direct towards a solution?
```
pip3 install datasets[audio]
```
### log
Requirement already satisfied: datasets[audio] in ./.virtualenvs/hubert/lib/python3.7/site-packages (1.18.3)
Requirement already satisfied: numpy>=1.17 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (1.21.5)
Requirement already satisfied: xxhash in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (3.0.0)
Requirement already satisfied: fsspec[http]>=2021.05.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (2022.2.0)
Requirement already satisfied: dill in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.3.4)
Requirement already satisfied: pandas in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (1.3.5)
Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.4.0)
Requirement already satisfied: packaging in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (21.3)
Requirement already satisfied: multiprocess in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.70.12.2)
Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (7.0.0)
Requirement already satisfied: tqdm>=4.62.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (4.63.1)
Requirement already satisfied: aiohttp in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (3.8.1)
Requirement already satisfied: importlib-metadata in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (4.11.3)
Requirement already satisfied: requests>=2.19.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (2.27.1)
Requirement already satisfied: librosa in ./.virtualenvs/hubert/lib/python3.7/site-packages (from datasets[audio]) (0.9.1)
Requirement already satisfied: pyyaml in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (6.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (4.1.1)
Requirement already satisfied: filelock in ./.virtualenvs/hubert/lib/python3.7/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets[audio]) (3.6.0)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from packaging->datasets[audio]) (3.0.7)
Requirement already satisfied: idna<4,>=2.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (3.3)
Requirement already satisfied: certifi>=2017.4.17 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (2021.10.8)
Requirement already satisfied: charset-normalizer~=2.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (2.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->datasets[audio]) (1.26.9)
Requirement already satisfied: attrs>=17.3.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (21.4.0)
Requirement already satisfied: frozenlist>=1.1.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.3.0)
Requirement already satisfied: aiosignal>=1.1.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.2.0)
Requirement already satisfied: yarl<2.0,>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (1.7.2)
Requirement already satisfied: asynctest==0.13.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (0.13.0)
Requirement already satisfied: multidict<7.0,>=4.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (6.0.2)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from aiohttp->datasets[audio]) (4.0.2)
Requirement already satisfied: zipp>=0.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from importlib-metadata->datasets[audio]) (3.7.0)
Requirement already satisfied: decorator>=4.0.10 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (5.1.1)
Requirement already satisfied: soundfile>=0.10.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.10.3.post1)
Requirement already satisfied: numba>=0.45.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.55.1)
Requirement already satisfied: pooch>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.6.0)
Requirement already satisfied: resampy>=0.2.2 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (0.2.2)
Requirement already satisfied: audioread>=2.1.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (2.1.9)
Requirement already satisfied: joblib>=0.14 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.1.0)
Requirement already satisfied: scipy>=1.2.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.7.3)
Requirement already satisfied: scikit-learn>=0.19.1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from librosa->datasets[audio]) (1.0.2)
Requirement already satisfied: python-dateutil>=2.7.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pandas->datasets[audio]) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pandas->datasets[audio]) (2022.1)
Requirement already satisfied: setuptools in ./.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa->datasets[audio]) (60.10.0)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa->datasets[audio]) (0.38.0)
Requirement already satisfied: appdirs>=1.3.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa->datasets[audio]) (1.4.4)
Requirement already satisfied: six>=1.5 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets[audio]) (1.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from scikit-learn>=0.19.1->librosa->datasets[audio]) (3.1.0)
Requirement already satisfied: cffi>=1.0 in ./.virtualenvs/hubert/lib/python3.7/site-packages (from soundfile>=0.10.2->librosa->datasets[audio]) (1.15.0)
Requirement already satisfied: pycparser in ./.virtualenvs/hubert/lib/python3.7/site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa->datasets[audio]) (2.21)
### reload
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
### log
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 33542.59it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:06<00:00, 22.28it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 21558.39it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2996.41it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 23431.87it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2697.52it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
### just to double-check as per your docs
```
pip3 install librosa torchaudio
```
### logs
Requirement already satisfied: librosa in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (0.9.1)
Requirement already satisfied: torchaudio in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (0.11.0+cu113)
Requirement already satisfied: audioread>=2.1.5 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (2.1.9)
Requirement already satisfied: joblib>=0.14 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.1.0)
Requirement already satisfied: packaging>=20.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (21.3)
Requirement already satisfied: scikit-learn>=0.19.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.0.2)
Requirement already satisfied: scipy>=1.2.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.7.3)
Requirement already satisfied: decorator>=4.0.10 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (5.1.1)
Requirement already satisfied: resampy>=0.2.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.2.2)
Requirement already satisfied: pooch>=1.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.6.0)
Requirement already satisfied: numpy>=1.17.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (1.21.5)
Requirement already satisfied: soundfile>=0.10.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.10.3.post1)
Requirement already satisfied: numba>=0.45.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from librosa) (0.55.1)
Requirement already satisfied: torch==1.11.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from torchaudio) (1.11.0+cu113)
Requirement already satisfied: typing-extensions in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from torch==1.11.0->torchaudio) (4.1.1)
Requirement already satisfied: setuptools in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa) (60.10.0)
Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from numba>=0.45.1->librosa) (0.38.0)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from packaging>=20.0->librosa) (3.0.7)
Requirement already satisfied: requests>=2.19.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa) (2.27.1)
Requirement already satisfied: appdirs>=1.3.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from pooch>=1.0->librosa) (1.4.4)
Requirement already satisfied: six>=1.3 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from resampy>=0.2.2->librosa) (1.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from scikit-learn>=0.19.1->librosa) (3.1.0)
Requirement already satisfied: cffi>=1.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from soundfile>=0.10.2->librosa) (1.15.0)
Requirement already satisfied: pycparser in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21)
Requirement already satisfied: charset-normalizer~=2.0.0 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.0.12)
Requirement already satisfied: certifi>=2017.4.17 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2021.10.8)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.9)
Requirement already satisfied: idna<4,>=2.5 in /home/neo/.virtualenvs/hubert/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.3)
### try loading again
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
### same error
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 33542.59it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:06<00:00, 22.28it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 21558.39it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2996.41it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 23431.87it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2697.52it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
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https://github.com/huggingface/datasets/issues/4000 | load_dataset error: sndfile library not found | Hi @i-am-neo, thanks again for your detailed report.
Our `datasets` library support for audio relies on a third-party Python library called `librosa`, which is installed when you do:
```shell
pip install datasets[audio]
```
However, the `librosa` library has a dependency on `soundfile`; and `soundfile` depends on a non-Python package called `sndfile`.
On Linux (which is your case), this must be installed manually using your operating system package manager, for example:
```shell
sudo apt-get install libsndfile1
```
Please, let me know if this works and if so, I will update our docs with all this information. | ## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| 95 | load_dataset error: sndfile library not found
## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
Hi @i-am-neo, thanks again for your detailed report.
Our `datasets` library support for audio relies on a third-party Python library called `librosa`, which is installed when you do:
```shell
pip install datasets[audio]
```
However, the `librosa` library has a dependency on `soundfile`; and `soundfile` depends on a non-Python package called `sndfile`.
On Linux (which is your case), this must be installed manually using your operating system package manager, for example:
```shell
sudo apt-get install libsndfile1
```
Please, let me know if this works and if so, I will update our docs with all this information. | [
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https://github.com/huggingface/datasets/issues/4000 | load_dataset error: sndfile library not found | @albertvillanova thanks, all good. The key is ```libsndfile1``` - it may help others to note that in your docs. I had installed libsndfile previously. | ## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
| 24 | load_dataset error: sndfile library not found
## Describe the bug
Can't load ami dataset
## Steps to reproduce the bug
```
python3 -c "from datasets import load_dataset; print(load_dataset('ami', 'headset-single', split='validation')[0])"
```
## Expected results
## Actual results
Downloading and preparing dataset ami/headset-single (download: 10.71 GiB, generated: 49.99 MiB, post-processed: Unknown size, total: 10.76 GiB) to /home/neo/.cache/huggingface/datasets/ami/headset-single/1.6.2/2accdf810f7c0585f78f4bcfa47684fbb980e35d29ecf126e6906dbecb872d9e...
AMI corpus cannot be downloaded using multi-processing. Setting number of downloaded processes `num_proc` to 1.
100%|██████████████████████████████████████████████████████| 136/136 [00:00<00:00, 36004.88it/s]
100%|█████████████████████████████████████████████████████████| 136/136 [00:01<00:00, 79.10it/s]
100%|████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 25343.23it/s]
100%|█████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 2874.78it/s]
100%|████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 27950.38it/s]
100%|█████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 2892.25it/s]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/load.py", line 1707, in load_dataset
use_auth_token=use_auth_token,
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 595, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/neo/.virtualenvs/hubert/lib/python3.7/site-packages/datasets/builder.py", line 690, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
sndfile library not found
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.3
- PyArrow version: 7.0.0
@albertvillanova thanks, all good. The key is ```libsndfile1``` - it may help others to note that in your docs. I had installed libsndfile previously. | [
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https://github.com/huggingface/datasets/issues/3996 | Audio.encode_example() throws an error when writing example from array | Thanks @polinaeterna for reporting this issue.
In relation to the decoding of MP3 audio files without torchaudio, I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio. But yes, nice to give an alternative to non-torchaudio users (with a big warning on performance). | ## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗 | 57 | Audio.encode_example() throws an error when writing example from array
## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗
Thanks @polinaeterna for reporting this issue.
In relation to the decoding of MP3 audio files without torchaudio, I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio. But yes, nice to give an alternative to non-torchaudio users (with a big warning on performance). | [
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https://github.com/huggingface/datasets/issues/3996 | Audio.encode_example() throws an error when writing example from array | > I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio.
Yeah, I know, but as far as I understand, some users just categorically don't want to have torchaudio in their environment. Anyway, it's just a more or less random example, they can use any library they like following the same logic (I'm just not a big expert in decoding utils so if you can give me some presentation / resources about that I would really appreciate it 🤗) | ## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗 | 93 | Audio.encode_example() throws an error when writing example from array
## Describe the bug
When trying to do `Audio().encode_example()` with preexisting array (see [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L73)), `sf.write()` throws you an error:
`TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7f4218c0db30>`
## Steps to reproduce the bug
### Sample code to reproduce the bug
```python
# download sample file
!wget https://huggingface.co/datasets/polinaeterna/test_encode_example/resolve/main/common_voice_vi_21824030.mp3
arr, sr = librosa.load("common_voice_vi_21824030.mp3")
Audio().encode_example({
"path": "common_voice_vi_21824030.mp3",
"array": arr,
"sampling_rate":sr
})
```
## Expected results
An encoded example (`{"bytes": b'....', "path": 'path'}`)
## Actual results
```python
TypeError Traceback (most recent call last)
Input In [3], in <module>
1 arr, sr = librosa.load("common_voice_vi_21824030.mp3")
----> 3 Audio().encode_example({
4 "path": "common_voice_vi_21824030.mp3",
5 "array": arr,
6 "sampling_rate":sr
7 })
File ~/workspace/datasets/src/datasets/features/audio.py:75, in Audio.encode_example(self, value)
73 elif isinstance(value, dict) and "array" in value:
74 buffer = BytesIO()
---> 75 sf.write(buffer, value["array"], value["sampling_rate"])
76 return {"bytes": buffer.getvalue(), "path": value.get("path")}
77 elif value.get("bytes") is not None or value.get("path") is not None:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:314, in write(file, data, samplerate, subtype, endian, format, closefd)
312 else:
313 channels = data.shape[1]
--> 314 with SoundFile(file, 'w', samplerate, channels,
315 subtype, endian, format, closefd) as f:
316 f.write(data)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:627, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
625 mode_int = _check_mode(mode)
626 self._mode = mode
--> 627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1416, in _create_info_struct(file, mode, samplerate, channels, format, subtype, endian)
1414 original_format = format
1415 if format is None:
-> 1416 format = _get_format_from_filename(file, mode)
1417 assert isinstance(format, (_unicode, str))
1418 else:
File ~/miniconda3/envs/datasets/lib/python3.8/site-packages/soundfile.py:1457, in _get_format_from_filename(file, mode)
1455 pass
1456 if format.upper() not in _formats and 'r' not in mode:
-> 1457 raise TypeError("No format specified and unable to get format from "
1458 "file extension: {0!r}".format(file))
1459 return format
TypeError: No format specified and unable to get format from file extension: <_io.BytesIO object at 0x7fd8daf88180>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets master
- Platform: Ubuntu 20.04
- Python version: python 3.8.12
- PyArrow version: 6.0.1
## Solution
I guess we just need to add `format` arg in [this line](https://github.com/huggingface/datasets/blob/master/src/datasets/features/audio.py#L75) like this:
```python
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
```
BTW discovered this when trying to decode audio in mp3 format without torchaudio (would be useful for TensorFlow users), like this:
```python
from datasets import load_dataset, Features, Audio
ds = load_dataset("common_voice", "vi", split="test")
ds = ds.remove_columns("audio")
ds.select(range(3)) # 3 samples just for testing
def load_mp3_with_librosa(example):
arr, sr = librosa.load(example["path"])
example["audio"] = {
"path": example["path"],
"array": arr,
"sampling_rate": sr
}
return example
updated_dataset = ds.map(lambda example: load_mp3_with_librosa(example),
features=Features(
{"audio": Audio(decode=False)}
))
```
@lhoestq @mariosasko @albertvillanova am I right in my logic? do we agree that we can set wav as the format? 🤗
> I remember Patrick made some tests and these had quite bad performance. That is why he proposed to support MP3 files only with torchaudio.
Yeah, I know, but as far as I understand, some users just categorically don't want to have torchaudio in their environment. Anyway, it's just a more or less random example, they can use any library they like following the same logic (I'm just not a big expert in decoding utils so if you can give me some presentation / resources about that I would really appreciate it 🤗) | [
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https://github.com/huggingface/datasets/issues/3993 | Streaming dataset + interleave + DataLoader hangs with multiple workers | Hi ! Thanks for reporting, could this be related to https://github.com/huggingface/datasets/issues/3950 ?
Currently streaming datasets only works in single process, but we're working on having in work in distributed setups as well :) (EDIT: done) | ## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.
## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader
en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')
next(iter(multilingual_dataset)) # works fairly fast
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
print(len(batch)) # prints nothing after 30 min of waiting
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
print(len(batch)) # prints right away
```
## Expected results
It should be able to iterate the dataset with multiple workers.
## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1
| 35 | Streaming dataset + interleave + DataLoader hangs with multiple workers
## Describe the bug
Interleaving multiple iterable datasets that use `load_dataset` on streaming mode hangs when passed to `torch.utils.data.DataLoader` with multiple workers.
## Steps to reproduce the bug
```python
from datasets import interleave_datasets, load_dataset
from torch.utils.data import DataLoader
en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
fr_dataset = load_dataset('oscar', "unshuffled_deduplicated_fr", split='train', streaming=True)
it_dataset = load_dataset('oscar', "unshuffled_deduplicated_it", split='train', streaming=True)
de_dataset = load_dataset('oscar', "unshuffled_deduplicated_de", split='train', streaming=True)
multilingual_dataset = interleave_datasets([en_dataset, fr_dataset, de_dataset, it_dataset])
multilingual_dataset = multilingual_dataset.with_format('torch')
next(iter(multilingual_dataset)) # works fairly fast
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=4)
for batch in dataloader:
print(len(batch)) # prints nothing after 30 min of waiting
dataloader = DataLoader(multilingual_dataset, batch_size=8, num_workers=0)
for batch in dataloader:
print(len(batch)) # prints right away
```
## Expected results
It should be able to iterate the dataset with multiple workers.
## Actual results
Prints with results with `next(iter(multilingual_dataset)) ` and `num_workers=0` but it prints nothing with `num_workers=4` or any number above 0.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.1.dev0
- `pytorch` version: 1.10.0+cu113
- Python version: 3.7
- PyArrow version: 6.0.1
Hi ! Thanks for reporting, could this be related to https://github.com/huggingface/datasets/issues/3950 ?
Currently streaming datasets only works in single process, but we're working on having in work in distributed setups as well :) (EDIT: done) | [
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https://github.com/huggingface/datasets/issues/3992 | Image column is not decoded in map when using with with_transform | Hi! This behavior stems from this line: https://github.com/huggingface/datasets/blob/799b817d97590ddc97cbd38d07469403e030de8c/src/datasets/arrow_dataset.py#L1919
Basically, the `Image`/`Audio` columns are decoded only if the `format_type` attribute is `None` (`set_format`/`with_format` and `set_transform`/`with_transform` assign a non-`None` value to it) and the `input_columns` param is not specified (see https://github.com/huggingface/datasets/issues/3756). We will remove these limitations soon.
| ## Describe the bug
Image column is not _decoded_ in **map** when using with `with_transform`
## Steps to reproduce the bug
```python
from datasets import Image, Dataset
def add_C(batch):
batch["C"] = batch["A"]
return batch
ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A", Image())
ds = ds.with_transform(lambda x: x) # <= This line causes the problem
ds = ds.map(add_C, batched=True)
print(ds[0])
```
## Expected results
```
{'C': <PIL.PngImagePlugin.PngImageFile>, ...}
```
## Actual results
```
{'C': {'bytes': None, 'path': 'image.png'}, ...}
```
If we remove the `with_transform` line, we get the expected result.
## Environment info
- `datasets` version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
| 45 | Image column is not decoded in map when using with with_transform
## Describe the bug
Image column is not _decoded_ in **map** when using with `with_transform`
## Steps to reproduce the bug
```python
from datasets import Image, Dataset
def add_C(batch):
batch["C"] = batch["A"]
return batch
ds = Dataset.from_dict({"A": ["image.png"]}).cast_column("A", Image())
ds = ds.with_transform(lambda x: x) # <= This line causes the problem
ds = ds.map(add_C, batched=True)
print(ds[0])
```
## Expected results
```
{'C': <PIL.PngImagePlugin.PngImageFile>, ...}
```
## Actual results
```
{'C': {'bytes': None, 'path': 'image.png'}, ...}
```
If we remove the `with_transform` line, we get the expected result.
## Environment info
- `datasets` version: 2.0.0
- Platform: Mac OSX
- Python version: 3.8.12
- PyArrow version: 7.0.0
Hi! This behavior stems from this line: https://github.com/huggingface/datasets/blob/799b817d97590ddc97cbd38d07469403e030de8c/src/datasets/arrow_dataset.py#L1919
Basically, the `Image`/`Audio` columns are decoded only if the `format_type` attribute is `None` (`set_format`/`with_format` and `set_transform`/`with_transform` assign a non-`None` value to it) and the `input_columns` param is not specified (see https://github.com/huggingface/datasets/issues/3756). We will remove these limitations soon.
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https://github.com/huggingface/datasets/issues/3990 | Improve AutomaticSpeechRecognition task template | There is an open PR to do that: #3364. I just haven't had time to finish it... | **Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
| 17 | Improve AutomaticSpeechRecognition task template
**Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
There is an open PR to do that: #3364. I just haven't had time to finish it... | [
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https://github.com/huggingface/datasets/issues/3990 | Improve AutomaticSpeechRecognition task template | > There is an open PR to do that: #3364. I just haven't had time to finish it...
😬 thanks... | **Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
| 20 | Improve AutomaticSpeechRecognition task template
**Is your feature request related to a problem? Please describe.**
[AutomaticSpeechRecognition task template](https://github.com/huggingface/datasets/blob/master/src/datasets/tasks/automatic_speech_recognition.py) is outdated as it uses path to audiofile as an audio column instead of a Audio feature itself (I guess it's because Audio feature didn't exist at the time this template was created).
**Describe the solution you'd like**
Change audio columns from string path to Audio feature.
> There is an open PR to do that: #3364. I just haven't had time to finish it...
😬 thanks... | [
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https://github.com/huggingface/datasets/issues/3986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | Hi ! I didn't managed to reproduce the issue. When you kill the process, is there any stacktrace that shows at what point in the code python is hanging ? | ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| 30 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
Hi ! I didn't managed to reproduce the issue. When you kill the process, is there any stacktrace that shows at what point in the code python is hanging ? | [
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https://github.com/huggingface/datasets/issues/3986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | Hi @lhoestq , I've traced the issue back to file locking. It's similar to this thread, using Lustre filesystem as well. https://github.com/huggingface/datasets/issues/329 . In this case the user was able to modify and add -o flock option while mounting and it solved the problem.
However in other cases such as mine, we do not have the permissions to modify the commands while mounting. I'm still trying to figure out a workaround. Any ideas how can we use a mounted Lustre filesystem with no flock option?
| ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| 85 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
Hi @lhoestq , I've traced the issue back to file locking. It's similar to this thread, using Lustre filesystem as well. https://github.com/huggingface/datasets/issues/329 . In this case the user was able to modify and add -o flock option while mounting and it solved the problem.
However in other cases such as mine, we do not have the permissions to modify the commands while mounting. I'm still trying to figure out a workaround. Any ideas how can we use a mounted Lustre filesystem with no flock option?
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https://github.com/huggingface/datasets/issues/3986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | Hi @kelvinAI , I've had this issue on our institution's system which uses Lustre (in addition to our compute nodes being siloed off from external network access). The workaround I made for downloading/loading datasets was to set the `$HFHOME` environment variable to a location on the node's local storage (SSD), effectively a location that gets cleared regularly and sometimes gets used for temporary or cached files which is pretty common, e.g. "scratch" storage. Maybe your sysadmins, if you have them, could point you to subdirectories on a node that aren't linked to the Lustre filesystem. After downloading to scratch I found that the transformers, modules, and metrics cached folders were fine to move to my user drives on the Lustre filesystem but cached datasets that had fingerprints still had some issues with filelock, so it would help to use the function `my_dataset.save_to_disk('path/on/lustre_fs')` and static class function `Dataset.load_from_disk('path/on/lustre_fs')`. In rough steps:
1. Initially download to scratch storage with `ds = datasets.load_dataset(dataset_name)`
2. Call `ds.save_to_disk(my_path_on_lustre)` with a path in your user space on the Lustre filesystem
3. Load datasets with `from datasets import Dataset; new_ds = Dataset.load_from_disk(my_path_on_lustre)`
Obviously this hinges on there existing scratch storage on the nodes you're using. Fingers crossed. | ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| 200 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
Hi @kelvinAI , I've had this issue on our institution's system which uses Lustre (in addition to our compute nodes being siloed off from external network access). The workaround I made for downloading/loading datasets was to set the `$HFHOME` environment variable to a location on the node's local storage (SSD), effectively a location that gets cleared regularly and sometimes gets used for temporary or cached files which is pretty common, e.g. "scratch" storage. Maybe your sysadmins, if you have them, could point you to subdirectories on a node that aren't linked to the Lustre filesystem. After downloading to scratch I found that the transformers, modules, and metrics cached folders were fine to move to my user drives on the Lustre filesystem but cached datasets that had fingerprints still had some issues with filelock, so it would help to use the function `my_dataset.save_to_disk('path/on/lustre_fs')` and static class function `Dataset.load_from_disk('path/on/lustre_fs')`. In rough steps:
1. Initially download to scratch storage with `ds = datasets.load_dataset(dataset_name)`
2. Call `ds.save_to_disk(my_path_on_lustre)` with a path in your user space on the Lustre filesystem
3. Load datasets with `from datasets import Dataset; new_ds = Dataset.load_from_disk(my_path_on_lustre)`
Obviously this hinges on there existing scratch storage on the nodes you're using. Fingers crossed. | [
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https://github.com/huggingface/datasets/issues/3986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | Hi @jpmcd , thanks for sharing your experience. For my case, the Lustre filesystem (with more storage space) is the scratch storage like the one you've mentioned. We have a local storage for each user but unfortunately there's not enough space in it to 'cache' huge datasets, hence that is why I tried changing HF_HOME to point to the scratch disk with more space and encountered the flock issue. Unfortunately I'm not aware of any viable solution to this for now so I simply fall back to using torch dataset. | ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| 90 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
Hi @jpmcd , thanks for sharing your experience. For my case, the Lustre filesystem (with more storage space) is the scratch storage like the one you've mentioned. We have a local storage for each user but unfortunately there's not enough space in it to 'cache' huge datasets, hence that is why I tried changing HF_HOME to point to the scratch disk with more space and encountered the flock issue. Unfortunately I'm not aware of any viable solution to this for now so I simply fall back to using torch dataset. | [
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https://github.com/huggingface/datasets/issues/3986 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface) | @jpmcd your comment saved me from pulling my hair out in frustration. Setting `HF_HOME` to a directory that's not on Lustre works like a charm. ✨ | ## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
| 26 | Dataset loads indefinitely after modifying default cache path (~/.cache/huggingface)
## Describe the bug
Dataset loads indefinitely after modifying cache path (~/.cache/huggingface)
If none of the environment variables are set, this custom dataset loads fine ( json-based dataset with custom dataset load script)
** Update: Transformer modules faces the same issue as well during loading
## A clear and concise description of what the bug is.
Issue:
- Dataset loading stalls / freezes indefinitely when HF_HOME is changed to a custom directory
- No error code, had to terminate the process
- There are some files created in the cache directory:
```
custom_cache_dir
| -- modules
| -- __init__.py
| -- datasets_modules
| -- __init__.py
| -- datasets
| -- __init__.py
| -- script.py (Dataset loading script)
| -- script.lock
```
There's no error nor any logs thrown so I'm out of ideas of how to to debug this. The custom dataset works fine if the default ~/.cache dir is used, but unfortunately it's out of space and we do not have permissions to modify the disk.
## Steps to reproduce the bug
What I've tried:
- Modifying HF_HOME (https://github.com/huggingface/transformers/issues/8703)
- Modifying HF_DATASETS_CACHE (https://huggingface.co/docs/datasets/v1.12.0/cache.html)
- Modifying cache_dir param during runtime
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('test_dataset', cache_dir='/path/to/new/cache')
```
- Disabling dataset cache
```python
>>> from datasets import set_caching_enabled
>>> set_caching_enabled(False)
```
## Expected results
Datasets should load / cache as usual with the only exception that cache directory is different
## Actual results
Any actions taken above to change the cache directory results in loading indefinitely without terminating.
## Environment info
- `transformers` version: 4.18.0.dev0
- Platform: Linux-4.15.0-54-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.4.0
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
@jpmcd your comment saved me from pulling my hair out in frustration. Setting `HF_HOME` to a directory that's not on Lustre works like a charm. ✨ | [
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https://github.com/huggingface/datasets/issues/3984 | Local and automatic tests fail | Hi ! To be able to run the tests, you need to install all the test dependencies and additional ones with
```
pip install -e .[tests]
pip install -r additional-tests-requirements.txt --no-deps
```
In particular, you probably need to `sacrebleu`. It looks like it wasn't able to instantiate `sacrebleu.TER` properly. | ## Describe the bug
Running the tests from CircleCI on a PR or locally fails, even with no changes. Tests seem to fail on `test_metric_common.py`
## Steps to reproduce the bug
```shell
git clone https://huggingface/datasets.git
cd datasets
```
```python
python -m pip install -e .
pytest
```
## Expected results
All tests passing
## Actual results
```
tests/test_metric_common.py:91:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.8.5/lib/python3.8/doctest.py:1336: in __run
exec(compile(example.source, filename, "single",
<doctest datasets_modules.metrics.ter.c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155.ter.Ter[3]>:1: in <module>
???
../datasets/src/datasets/metric.py:430: in compute
output = self._compute(**inputs, **compute_kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Metric(name: "ter", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Val...ences=references)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 6.5}
""", stored examples: 0)
predictions = ['hello there general kenobi', 'foo bar foobar']
references = [['hello there general kenobi', 'hello there !'], ['foo bar foobar', 'foo bar foobar']]
normalized = False, no_punct = False, asian_support = False, case_sensitive = False
def _compute(
self,
predictions,
references,
normalized: bool = False,
no_punct: bool = False,
asian_support: bool = False,
case_sensitive: bool = False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> sb_ter = TER(normalized, no_punct, asian_support, case_sensitive)
E TypeError: __init__() takes 2 positional arguments but 5 were given
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/metrics/ter/c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155/ter.py:130: TypeError
------------------------------ Captured stdout call -------------------------------
Trying:
predictions = ["hello there general kenobi", "foo bar foobar"]
Expecting nothing
ok
Trying:
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
Expecting nothing
ok
Trying:
ter = datasets.load_metric("ter")
Expecting nothing
ok
Trying:
results = ter.compute(predictions=predictions, references=references)
Expecting nothing
================================ warnings summary =================================
../.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
from imp import load_source
../datasets/src/datasets/commands/test.py:35
/home/markussagen/datasets/src/datasets/commands/test.py:35: PytestCollectionWarning: cannot collect test class 'TestCommand' because it has a __init__ constructor (from: tests/commands/test_test.py)
class TestCommand(BaseDatasetsCLICommand):
tests/commands/test_test.py:33
/home/markussagen/mydataset/tests/commands/test_test.py:33: PytestCollectionWarning: cannot collect test class 'TestCommandArgs' because it has a __new__ constructor (from: tests/commands/test_test.py)
class TestCommandArgs:
tests/test_arrow_dataset.py: 760 warnings
tests/test_formatting.py: 60 warnings
tests/test_search.py: 31 warnings
tests/features/test_array_xd.py: 117 warnings
/home/markussagen/datasets/src/datasets/formatting/formatting.py:197: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
(isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
tests/test_arrow_dataset.py: 154 warnings
tests/features/test_array_xd.py: 1 warning
/home/markussagen/datasets/src/datasets/formatting/formatting.py:201: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
tests/test_arrow_dataset.py: 60 warnings
/home/markussagen/datasets/src/datasets/arrow_dataset.py:3105: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
elif np.issubdtype(values.dtype, np.str):
tests/test_arrow_dataset.py: 138 warnings
tests/test_formatting.py: 21 warnings
/home/markussagen/datasets/src/datasets/formatting/tf_formatter.py:69: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
data_struct.dtype == np.object
tests/test_arrow_dataset.py: 240 warnings
tests/test_formatting.py: 20 warnings
/home/markussagen/datasets/src/datasets/formatting/torch_formatter.py:49: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
tests/test_arrow_dataset.py: 12 warnings
tests/test_search.py: 2 warnings
tests/features/test_array_xd.py: 6 warnings
tests/features/test_image.py: 4 warnings
/home/markussagen/datasets/src/datasets/features/features.py:1129: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
[0] + [len(arr) for arr in l_arr], dtype=np.object
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_banking77
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/banking77/aec0289529599d4572d76ab00c8944cb84f88410ad0c9e7da26189d31f62a55b/banking77.py:24: DeprecationWarning: invalid escape sequence \~
_CITATION = """\
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_universal_dependencies
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/universal_dependencies/065e728dfe9a8371434a6e87132c2386a6eacab1a076d3a12aa417b994e6ef7d/universal_dependencies.py:6: DeprecationWarning: invalid escape sequence \=
_CITATION = """\
tests/test_filesystem.py: 105 warnings
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/responses/__init__.py:398: DeprecationWarning: stream argument is deprecated. Use stream parameter in request directly
warn(
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
/home/markussagen/datasets/src/datasets/formatting/jax_formatter.py:57: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # jax arrays cannot be instantied from an array of objects
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3567: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
lax._check_user_dtype_supported(dtype, "array")
tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html
if obj.zone == 'local':
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features
_audio
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/librosa/core/constantq.py:1059: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
dtype=np.complex,
tests/features/test_array_xd.py::test_array_xd_with_none
/home/markussagen/mydataset/tests/features/test_array_xd.py:338: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
assert isinstance(arr, np.ndarray) and arr.dtype == np.object and arr.shape == (3,)
-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================= short test summary info =============================
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bleurt - I...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_chrf - Att...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_code_eval
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_comet - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_coval - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_ter - Type...
```
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-5.16.11-76051611-generic-x86_64-with-glibc2.33
- Python version: 3.8.5
- PyArrow version: 5.0.0
| 49 | Local and automatic tests fail
## Describe the bug
Running the tests from CircleCI on a PR or locally fails, even with no changes. Tests seem to fail on `test_metric_common.py`
## Steps to reproduce the bug
```shell
git clone https://huggingface/datasets.git
cd datasets
```
```python
python -m pip install -e .
pytest
```
## Expected results
All tests passing
## Actual results
```
tests/test_metric_common.py:91:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.pyenv/versions/3.8.5/lib/python3.8/doctest.py:1336: in __run
exec(compile(example.source, filename, "single",
<doctest datasets_modules.metrics.ter.c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155.ter.Ter[3]>:1: in <module>
???
../datasets/src/datasets/metric.py:430: in compute
output = self._compute(**inputs, **compute_kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = Metric(name: "ter", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Val...ences=references)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 6.5}
""", stored examples: 0)
predictions = ['hello there general kenobi', 'foo bar foobar']
references = [['hello there general kenobi', 'hello there !'], ['foo bar foobar', 'foo bar foobar']]
normalized = False, no_punct = False, asian_support = False, case_sensitive = False
def _compute(
self,
predictions,
references,
normalized: bool = False,
no_punct: bool = False,
asian_support: bool = False,
case_sensitive: bool = False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
raise ValueError("Sacrebleu requires the same number of references for each prediction")
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
> sb_ter = TER(normalized, no_punct, asian_support, case_sensitive)
E TypeError: __init__() takes 2 positional arguments but 5 were given
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/metrics/ter/c0cfb5adedac7eb15ffa47bba6a70fabd80f3eb906ee508abf5e1906285d1155/ter.py:130: TypeError
------------------------------ Captured stdout call -------------------------------
Trying:
predictions = ["hello there general kenobi", "foo bar foobar"]
Expecting nothing
ok
Trying:
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
Expecting nothing
ok
Trying:
ter = datasets.load_metric("ter")
Expecting nothing
ok
Trying:
results = ter.compute(predictions=predictions, references=references)
Expecting nothing
================================ warnings summary =================================
../.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/hdfs/config.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
from imp import load_source
../datasets/src/datasets/commands/test.py:35
/home/markussagen/datasets/src/datasets/commands/test.py:35: PytestCollectionWarning: cannot collect test class 'TestCommand' because it has a __init__ constructor (from: tests/commands/test_test.py)
class TestCommand(BaseDatasetsCLICommand):
tests/commands/test_test.py:33
/home/markussagen/mydataset/tests/commands/test_test.py:33: PytestCollectionWarning: cannot collect test class 'TestCommandArgs' because it has a __new__ constructor (from: tests/commands/test_test.py)
class TestCommandArgs:
tests/test_arrow_dataset.py: 760 warnings
tests/test_formatting.py: 60 warnings
tests/test_search.py: 31 warnings
tests/features/test_array_xd.py: 117 warnings
/home/markussagen/datasets/src/datasets/formatting/formatting.py:197: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
(isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape))
tests/test_arrow_dataset.py: 154 warnings
tests/features/test_array_xd.py: 1 warning
/home/markussagen/datasets/src/datasets/formatting/formatting.py:201: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
return np.array(array, copy=False, **{**self.np_array_kwargs, "dtype": np.object})
tests/test_arrow_dataset.py: 60 warnings
/home/markussagen/datasets/src/datasets/arrow_dataset.py:3105: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
elif np.issubdtype(values.dtype, np.str):
tests/test_arrow_dataset.py: 138 warnings
tests/test_formatting.py: 21 warnings
/home/markussagen/datasets/src/datasets/formatting/tf_formatter.py:69: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
data_struct.dtype == np.object
tests/test_arrow_dataset.py: 240 warnings
tests/test_formatting.py: 20 warnings
/home/markussagen/datasets/src/datasets/formatting/torch_formatter.py:49: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
tests/test_arrow_dataset.py: 12 warnings
tests/test_search.py: 2 warnings
tests/features/test_array_xd.py: 6 warnings
tests/features/test_image.py: 4 warnings
/home/markussagen/datasets/src/datasets/features/features.py:1129: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
[0] + [len(arr) for arr in l_arr], dtype=np.object
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_banking77
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/banking77/aec0289529599d4572d76ab00c8944cb84f88410ad0c9e7da26189d31f62a55b/banking77.py:24: DeprecationWarning: invalid escape sequence \~
_CITATION = """\
tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_universal_dependencies
/tmp/pytest-of-markussagen/pytest-1/cache/modules/datasets_modules/datasets/universal_dependencies/065e728dfe9a8371434a6e87132c2386a6eacab1a076d3a12aa417b994e6ef7d/universal_dependencies.py:6: DeprecationWarning: invalid escape sequence \=
_CITATION = """\
tests/test_filesystem.py: 105 warnings
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/responses/__init__.py:398: DeprecationWarning: stream argument is deprecated. Use stream parameter in request directly
warn(
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
tests/test_formatting.py::FormatterTest::test_jax_formatter_np_array_kwargs
/home/markussagen/datasets/src/datasets/formatting/jax_formatter.py:57: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if data_struct.dtype == np.object: # jax arrays cannot be instantied from an array of objects
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
tests/test_formatting.py::FormatterTest::test_jax_formatter
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3567: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in array is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
lax._check_user_dtype_supported(dtype, "array")
tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/apscheduler/util.py:95: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html
if obj.zone == 'local':
tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features
_audio
/home/markussagen/.pyenv/versions/3.8.5/envs/huggingface/lib/python3.8/site-packages/librosa/core/constantq.py:1059: DeprecationWarning: `np.complex` is a deprecated alias for the builtin `complex`. To silence this warning, use `complex` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.complex128` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
dtype=np.complex,
tests/features/test_array_xd.py::test_array_xd_with_none
/home/markussagen/mydataset/tests/features/test_array_xd.py:338: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
assert isinstance(arr, np.ndarray) and arr.dtype == np.object and arr.shape == (3,)
-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================= short test summary info =============================
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_bleurt - I...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_chrf - Att...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_code_eval
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_comet - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_competition_math
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_coval - Im...
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_perplexity
FAILED tests/test_metric_common.py::LocalMetricTest::test_load_metric_ter - Type...
```
## Environment info
- `datasets` version: 2.0.1.dev0
- Platform: Linux-5.16.11-76051611-generic-x86_64-with-glibc2.33
- Python version: 3.8.5
- PyArrow version: 5.0.0
Hi ! To be able to run the tests, you need to install all the test dependencies and additional ones with
```
pip install -e .[tests]
pip install -r additional-tests-requirements.txt --no-deps
```
In particular, you probably need to `sacrebleu`. It looks like it wasn't able to instantiate `sacrebleu.TER` properly. | [
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https://github.com/huggingface/datasets/issues/3983 | Infinitely attempting lock | Hi ! Thanks for reporting. We're using filelock` as our locking mechanism.
Can you try deleting the .lock file mentioned in the logs and try again ? Make sure that no other process is generating the `cnn_dailymail` dataset.
If it doesn't work, could you try to set up a lock using the latest version of filelock` and see if it works ?
```
pip install filelock
```
here is a code example from the `filelock` documentation that you can try:
```python
from filelock import FileLock
lock = FileLock("test.txt.lock")
with lock:
with open("test.txt", "a") as f:
f.write("foo")
``` | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | 97 | Infinitely attempting lock
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
Hi ! Thanks for reporting. We're using filelock` as our locking mechanism.
Can you try deleting the .lock file mentioned in the logs and try again ? Make sure that no other process is generating the `cnn_dailymail` dataset.
If it doesn't work, could you try to set up a lock using the latest version of filelock` and see if it works ?
```
pip install filelock
```
here is a code example from the `filelock` documentation that you can try:
```python
from filelock import FileLock
lock = FileLock("test.txt.lock")
with lock:
with open("test.txt", "a") as f:
f.write("foo")
``` | [
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https://github.com/huggingface/datasets/issues/3983 | Infinitely attempting lock | I ran into this problem on my school server as well? Any update on how we can solve it? Thanks! | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | 20 | Infinitely attempting lock
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
I ran into this problem on my school server as well? Any update on how we can solve it? Thanks! | [
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] |
https://github.com/huggingface/datasets/issues/3983 | Infinitely attempting lock | Have you tried running the code above to check if FileLock works in your setup ? You may also be interested in checking the https://github.com/tox-dev/filelock repository for issues | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | 28 | Infinitely attempting lock
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
Have you tried running the code above to check if FileLock works in your setup ? You may also be interested in checking the https://github.com/tox-dev/filelock repository for issues | [
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https://github.com/huggingface/datasets/issues/3983 | Infinitely attempting lock | Can you try using a different cache directory ? Maybe there are permissions issues with the default one.
You can do so by passing `cache_dir=...` to load_dataset() | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | 27 | Infinitely attempting lock
I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this?
Can you try using a different cache directory ? Maybe there are permissions issues with the default one.
You can do so by passing `cache_dir=...` to load_dataset() | [
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https://github.com/huggingface/datasets/issues/3978 | I can't view HFcallback dataset for ASR Space | the dataset viewer is working on this dataset. I imagine the issue is that we would expect to be able to listen to the audio files in the `Please Record Your Voice file` column, right?
maybe @lhoestq or @albertvillanova could help
<img width="1019" alt="Capture d’écran 2022-03-24 à 17 36 20" src="https://user-images.githubusercontent.com/1676121/159966006-57dcf8f7-b65f-4200-ac8c-66859318a8bb.png">
| ## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
| 51 | I can't view HFcallback dataset for ASR Space
## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
the dataset viewer is working on this dataset. I imagine the issue is that we would expect to be able to listen to the audio files in the `Please Record Your Voice file` column, right?
maybe @lhoestq or @albertvillanova could help
<img width="1019" alt="Capture d’écran 2022-03-24 à 17 36 20" src="https://user-images.githubusercontent.com/1676121/159966006-57dcf8f7-b65f-4200-ac8c-66859318a8bb.png">
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https://github.com/huggingface/datasets/issues/3978 | I can't view HFcallback dataset for ASR Space | The structure of the dataset is not supported. Only the CSV file is parsed and the audio files are ignored.
We're working on supporting audio datasets with a specific structure in #3963 | ## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
| 32 | I can't view HFcallback dataset for ASR Space
## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
The structure of the dataset is not supported. Only the CSV file is parsed and the audio files are ignored.
We're working on supporting audio datasets with a specific structure in #3963 | [
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https://github.com/huggingface/datasets/issues/3978 | I can't view HFcallback dataset for ASR Space | Current error:
```
Error code: StreamingRowsError
Exception: LibsndfileError
Message: Error opening <File-like object HfFileSystem, datasets/kingabzpro/Urdu-ASR-flags@6a8878cfe3a41343fa86ec8b4254209fe56a0f0d/Please Record Your Voice/0.wav>: Format not recognised.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 263, in get_rows_or_raise
return get_rows(
File "/src/services/worker/src/worker/utils.py", line 204, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 241, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1357, in __iter__
example = _apply_feature_types_on_example(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1051, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1902, in decode_example
return {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1903, in <dictcomp>
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 187, in decode_example
array, sampling_rate = sf.read(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 285, in read
with SoundFile(file, 'r', samplerate, channels,
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <File-like object HfFileSystem, datasets/kingabzpro/Urdu-ASR-flags@6a8878cfe3a41343fa86ec8b4254209fe56a0f0d/Please Record Your Voice/0.wav>: Format not recognised.
```
Maybe switch to a discussion here? https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags/discussions. cc @albertvillanova | ## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
| 169 | I can't view HFcallback dataset for ASR Space
## Dataset viewer issue for '*Urdu-ASR-flags*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags)*
*I think dataset should show some thing and if you want me to add script, please show me the documentation. I thought this was suppose to be automatic task.*
Am I the one who added this dataset ? Yes
Current error:
```
Error code: StreamingRowsError
Exception: LibsndfileError
Message: Error opening <File-like object HfFileSystem, datasets/kingabzpro/Urdu-ASR-flags@6a8878cfe3a41343fa86ec8b4254209fe56a0f0d/Please Record Your Voice/0.wav>: Format not recognised.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 263, in get_rows_or_raise
return get_rows(
File "/src/services/worker/src/worker/utils.py", line 204, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 241, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1357, in __iter__
example = _apply_feature_types_on_example(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1051, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1902, in decode_example
return {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1903, in <dictcomp>
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 187, in decode_example
array, sampling_rate = sf.read(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 285, in read
with SoundFile(file, 'r', samplerate, channels,
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
File "/src/services/worker/.venv/lib/python3.9/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <File-like object HfFileSystem, datasets/kingabzpro/Urdu-ASR-flags@6a8878cfe3a41343fa86ec8b4254209fe56a0f0d/Please Record Your Voice/0.wav>: Format not recognised.
```
Maybe switch to a discussion here? https://huggingface.co/datasets/kingabzpro/Urdu-ASR-flags/discussions. cc @albertvillanova | [
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] |
https://github.com/huggingface/datasets/issues/3973 | ConnectionError and SSLError | Hi ! You can download the `oscar.py` file from this repository at `/datasets/oscar/oscar.py`.
Then you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:
```python
load_dataset("path/to/oscar.py", "unshuffled_deduplicated_it")
``` | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| 32 | ConnectionError and SSLError
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
Hi ! You can download the `oscar.py` file from this repository at `/datasets/oscar/oscar.py`.
Then you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:
```python
load_dataset("path/to/oscar.py", "unshuffled_deduplicated_it")
``` | [
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] |
https://github.com/huggingface/datasets/issues/3973 | ConnectionError and SSLError | it works,but another error occurs.
```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (SSLError(MaxRetryError("HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
I can access `https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt` and `https://aws.amazon.com/cn/s3/` directly, so why it reports a SSLError, should I need tomodify the host file？ | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| 49 | ConnectionError and SSLError
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
it works,but another error occurs.
```
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (SSLError(MaxRetryError("HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
I can access `https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt` and `https://aws.amazon.com/cn/s3/` directly, so why it reports a SSLError, should I need tomodify the host file？ | [
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] |
https://github.com/huggingface/datasets/issues/3973 | ConnectionError and SSLError | you are so wise!
it report [ConnectionError] in python 3.9.7
and works well in python 3.8.12
I need you help again: how can I specify the path for download files?
the data is too large and my C hardware is not enough | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| 42 | ConnectionError and SSLError
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
you are so wise!
it report [ConnectionError] in python 3.9.7
and works well in python 3.8.12
I need you help again: how can I specify the path for download files?
the data is too large and my C hardware is not enough | [
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] |
https://github.com/huggingface/datasets/issues/3973 | ConnectionError and SSLError | Cool ! And you can specify the path for download files with to the `cache_dir` parameter:
```python
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it', cache_dir='path/to/directory') | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| 26 | ConnectionError and SSLError
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
Cool ! And you can specify the path for download files with to the `cache_dir` parameter:
```python
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it', cache_dir='path/to/directory') | [
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] |
https://github.com/huggingface/datasets/issues/3973 | ConnectionError and SSLError | It takes me some days to download data completely, Despise sometimes it occurs again, change py version is feasible way to avoid this ConnectionEror.
parameter `cache_dir` works well, thanks for your kindness again! | code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
| 33 | ConnectionError and SSLError
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
It takes me some days to download data completely, Despise sometimes it occurs again, change py version is feasible way to avoid this ConnectionEror.
parameter `cache_dir` works well, thanks for your kindness again! | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | I guess the cache got corrupted due to a previous issue with Google Drive service.
The cache should be regenerated, e.g. by passing `download_mode="force_redownload"`.
CC: @severo | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 26 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
I guess the cache got corrupted due to a previous issue with Google Drive service.
The cache should be regenerated, e.g. by passing `download_mode="force_redownload"`.
CC: @severo | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | Note that the dataset preview uses its own cache, not `datasets`' cache. So `download_mode="force_redownload"` doesn't help. But yes indeed the cache must be refreshed.
The CNN Dailymail dataste is currently hosted on Google Drive, which is an unreliable host and we've had many issues with it. Unless we found another most reliable host for the data, we will keep running into issues from time to time.
At Hugging Face we're not allowed to host the CNN Dailymail data by ourselves AFAIK | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 81 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
Note that the dataset preview uses its own cache, not `datasets`' cache. So `download_mode="force_redownload"` doesn't help. But yes indeed the cache must be refreshed.
The CNN Dailymail dataste is currently hosted on Google Drive, which is an unreliable host and we've had many issues with it. Unless we found another most reliable host for the data, we will keep running into issues from time to time.
At Hugging Face we're not allowed to host the CNN Dailymail data by ourselves AFAIK | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | I remove the tag dataset-viewer, since it's more an issue with the hosting on Google Drive | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 16 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
I remove the tag dataset-viewer, since it's more an issue with the hosting on Google Drive | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | Sounds good. I was looking for another host of this dataset but couldn't find any (yet) | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 16 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
Sounds good. I was looking for another host of this dataset but couldn't find any (yet) | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | It seems like the issue is with the streaming mode, not with the hosting:
```python
>>> import datasets
>>> dataset = datasets.load_dataset('cnn_dailymail', name="3.0.0", split="train", streaming=True, download_mode="force_redownload")
Downloading builder script: 9.35kB [00:00, 10.2MB/s]
Downloading metadata: 9.50kB [00:00, 12.2MB/s]
>>> len(list(dataset))
0
>>> dataset = datasets.load_dataset('cnn_dailymail', name="3.0.0", split="train", streaming=False)
Reusing dataset cnn_dailymail (/home/slesage/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234)
>>> len(dataset)
287113
```
Note, in particular, that the streaming mode is failing silently, returning 0 row while I would have expected an exception instead. The result is that the dataset viewer shows `No data` instead of a detailed error.
<img width="1511" alt="Capture d’écran 2022-04-12 à 11 50 46" src="https://user-images.githubusercontent.com/1676121/162935341-d50f1e73-d053-41d4-917f-e79708a0ca23.png">
| ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 101 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
It seems like the issue is with the streaming mode, not with the hosting:
```python
>>> import datasets
>>> dataset = datasets.load_dataset('cnn_dailymail', name="3.0.0", split="train", streaming=True, download_mode="force_redownload")
Downloading builder script: 9.35kB [00:00, 10.2MB/s]
Downloading metadata: 9.50kB [00:00, 12.2MB/s]
>>> len(list(dataset))
0
>>> dataset = datasets.load_dataset('cnn_dailymail', name="3.0.0", split="train", streaming=False)
Reusing dataset cnn_dailymail (/home/slesage/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234)
>>> len(dataset)
287113
```
Note, in particular, that the streaming mode is failing silently, returning 0 row while I would have expected an exception instead. The result is that the dataset viewer shows `No data` instead of a detailed error.
<img width="1511" alt="Capture d’écran 2022-04-12 à 11 50 46" src="https://user-images.githubusercontent.com/1676121/162935341-d50f1e73-d053-41d4-917f-e79708a0ca23.png">
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | Well this is because the host (Google Drive) returns a document that is not the actual data, but an error page | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 21 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
Well this is because the host (Google Drive) returns a document that is not the actual data, but an error page | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | Yes it definitely should ! I don't have the bandwidth to work on this right now though | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 17 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
Yes it definitely should ! I don't have the bandwidth to work on this right now though | [
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https://github.com/huggingface/datasets/issues/3969 | Cannot preview cnn_dailymail dataset | Indeed, streaming was not supported: tgz archives were not properly iterated.
I've opened a PR to support streaming.
However, keep in mind that Google Drive will keep generating issues from time to time, like 403,... | ## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
| 35 | Cannot preview cnn_dailymail dataset
## Dataset viewer issue for '*cnn_dailymail*'
**Link:** https://huggingface.co/datasets/cnn_dailymail
*short description of the issue*
Am I the one who added this dataset ? Yes-No
Indeed, streaming was not supported: tgz archives were not properly iterated.
I've opened a PR to support streaming.
However, keep in mind that Google Drive will keep generating issues from time to time, like 403,... | [
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